Answers to pressing questions in high-energy physics may lie in electroweak symmetry breaking, the phenomenon for explaining why the weak and electromagnetic forces are different. From solving the mystery of dark energy to string theory, our entire philosophy depends on the unknown physics at the electroweak scale. The hunt for the elusive Higgs boson has gone on for almost half a century. Its discovery was finally announced on July 4, 2012. The discovery of the Higgs boson is even more significant than often discussed. This grand experimental achievement in the largest, most powerful machine ever built, the Large Hadron Collider, marks a far wider scientific, philosophical and intellectual triumph – and one that spans human history from the dawn of civilization. It has to do with the idea of symmetry: amazingly, the Higgs boson was predicted to exist not for any physical reasons, but on strictly mathematical grounds based on arcane symmetries usually studied in "pure" mathematics. And the search for these symmetries involves a major quest that began with the Babylonians and Egyptians and continued to the ancient Greeks, the Arabs, medieval Europe, and on through the 19th century to our own time. This talk will begin with a brief overview of particle physics and why the Higgs Boson is so important and how it completes the symmetry. It will then explain how one goes about searching for this particle and expand the critical role simulation efforts played not only in the final analysis but also in designing the detector systems, which truly are “modern marvels.” I will discuss with some other highlights from these experiments and what to expect as the LHC gears up to come back on line in early 2015 at its design energy, nearly double its current 8 GeV operating point. Finally, I will close with a few words on particle physics and society and how the search for the perhaps esoteric has benefited society.

This talk will begin with an overview of the life of John Swanson, showing that his success was due to a community effort, and celebrating the people and institutions which made his engineering contributions possible. A milestone in this path is the awarding of the John Fritz Medal, said to be “the highest award in the engineering profession”. The rest of this talk will be a non-technical history of ANSYS, the world’s premier engineering simulation software. Discussion will include the technical, engineering, and business factors and decisions which put ANSYS on the path to global success. Questions will be welcome at the end of the talk, and at the conference after the luncheon address.

Driven by the widespread availability of commercial multiprocessor systems and advances in computer networking, the parallel and distributed simulation field emerged and flourished in the late 1970s and 1980s. The field has evolved since that time to address critical issues such as synchronization and interoperability, and remains an active area of research to this day. Many impressive successes have been reported to date. Today, new platforms ranging from massively parallel, heterogeneous supercomputers and cloud computing environments as well as broader technology trends such as “big data” and the Internet of Things present new challenges and opportunities.

This presentation will review work in the parallel and distributed simulation field from seminal research in the 1970s and 80s to important successes that illustrate the potential offered by this technology. Key impediments that have prevented the technology from achieving more widespread adoption by the general modeling and simulation community are discussed as well as important challenges that remain in exploiting new and emerging computing platforms and technology trends.

Agent-based modeling and simulation (ABMS) is a an approach to modeling systems comprised of autonomous, interacting agents. Computational advances are making it possible to develop agent-based models in a variety of application areas, including areas where simulation has not been extensively applied. Applications range from modeling agent behavior in supply chains, consumer goods markets, and financial markets, to predicting the spread of epidemics and understanding the factors responsible for the fall of ancient civilizations. Progress suggests that ABMS could have far-reaching effects on the way that businesses use computer models to support decision-making and how researchers use models as electronic laboratories. Some contend that ABMS “is a third way of doing science” and could augment traditional discovery methods for knowledge generation. This brief tutorial introduces agent-based modeling by describing key concepts of ABMS, discussing some illustrative applications, and addressing toolkits and methods for developing agent-based models.

We provide a tutorial overview of simulation optimization methods, including statistical ranking \& selection (R\&S) methods such as indifference-zone procedures, optimal computing budget allocation (OCBA), and Bayesian value of information approaches; random search methods; sample average approximation (SAA); response surface methodology (RSM); and stochastic approximation (SA). We provide high-level descriptions of each of the approaches, as well as some comparisons of their characteristics and relative strengths; simple examples will be used to illustrate the different approaches in the talk. We then describe some recent research in two areas of simulation optimization: stochastic approximation and the use of direct stochastic gradients for simulation metamodels.

Although a corporation does not own all of its supply chain, the entire chain is responsible for product delivery and customer satisfaction. As one of several methodologies available for supply chain analysis, simulation has distinct advantages and disadvantages when compared to other analysis methodologies. This tutorial will detail the reasons why one would want to use simulation as the analysis methodology to evaluate supply chains, its advantages and disadvantages against other analysis methodologies such as optimization, and business scenarios where simulation can find cost reductions that other methodologies would miss.

There is a boundary separating analytic methodology and simulation methodology. If a problem involves the flipping of coins or the rolling of dice, for example, analytic methods are generally employed. If a problem involves a complex series of queues with a nonstationary arrival stream, discrete-event simulation methods are generally employed. This tutorial considers problems that are near the boundary between analytic methods and simulation methods. We use the symbolic-based language APPL (A Probability Programming Language) to perform operations on random variables to address these problems. The problems considered are the infinite bootstrap, the probability distribution of the Kolmogorov-Smirnov test statistic, the distribution of the time to complete a stochastic activity network, finding a lower bound on system reliability, Benford's law, finding the probability distribution and variance-covariance matrix of sojourn times in a queueing model, probability distribution relationships, testing random numbers, bivariate transformations, and time series models.

Simulation models often have many input factors, and determining which ones have a significant impact on performance measures (responses) of interest can be a difficult task. The common approach of changing one factor at a time is statistically inefficient and, more importantly, is very often just incorrect, because for many models factors interact to impact on the responses. In this tutorial we present an introduction to design of experiments specifically for simulation modeling, whose major goal is to determine the important factors with the least amount of simulating. We discuss classical experimental designs such as full factorial, fractional factorial, and central composite followed by a presentation on Latin hypercube designs, which are designed for the complex, nonlinear responses typically associated with simulation models.

All during the past half-century, the environment of computing applications has evolved from large, comparatively slow mainframes with storage small and expensive by today’s standards to desktops, laptops, cloud computing, fast computation, graphical capabilities, and capacious flash drives carried in pocket or purse. All this time, discrete-event process simulation has steadily grown in power, ease of application, availability of expertise, and breadth of applications to business challenges in manufacturing, supply chain operations, health care, call centers, retailing, transport networks, and more. Manufacturing applications were among the first, and are now among the most frequent and most beneficial, applications of simulation. In this paper, the road, from newcomer to simulation in manufacturing to contented beneficiary of its regular and routine use, is mapped and signposted.

A simulation project is much more than building a model and the skills required for success go well beyond knowing a particular simulation tool. A 30 year veteran discusses some important steps to enable project success and some cautions and tips to help avoid common traps. This presentation discusses aspects of modeling that are often missed by new and aspiring simulationists. In particular, tips and advice are provided to help you avoid some common traps and help ensure that your early projects are successful. The first four topics dealing with defining project objectives, understanding the system, creating a functional specification, and managing the project are often given inadequate attention by beginning modelers. The latter sections dealing with building, verifying, validating, and presenting the model offer some insight into some proven approaches.

While the management of a simulation project has many of the characteristics of traditional project management, it also has a number of unique issues that must be addressed. This paper will address the common, the not-so-common, and the treacherous aspects of simulation project management. A simulation specific series of steps will be presented. The paper also includes examples of past projects, the issues that arose, and how they were resolved.

Like a system model in information systems and software engineering (IS/SE), also a system model in simulation engineering mainly consists of an information model and a process model. In the fields of IS/SE there are widely used standards such as the Class Diagrams of the Unified Modeling Language (UML) for making information models, and the Business Process Modeling Notation (BPMN) for making process models. This tutorial shows how to use UML class diagrams and BPMN process models at all three levels of model-driven simulation engineering: for making conceptual simulation models, for making platform-independent simulation design models, and for making platform-specific, executable simulation models.

In this paper verification and validation of simulation models are discussed. Different approaches to deciding model validity are described and a graphical paradigm that relates verification and validation to the model development process is presented and explained. Conceptual model validity, model verification, operational validity, and data validity are discussed and a recommended procedure for model validation is presented.

This paper provides simulation practitioners and consumers with a grounding in how discrete-event simulation software works. Topics include discrete-event systems; entities, resources, control elements and operations; simulation runs; entity states; entity lists; and their management. The implementations of these generic ideas in AutoMod, SLX, ExtendSim, and Simio are described. The paper concludes with several examples of “why it matters” for modelers to know how their simulation software works, including discussion of AutoMod, SLX, ExtendSim, Simio, Arena, ProModel, and GPSS/H.

This tutorial explains how to develop dedicated simulators for executing event graph models and activity cycle diagram (ACD) models. An event-graph simulator template and an ACD simulator template are presented in pseudo code form, together with example C# implementations for a simple discrete-event system. A list of the simulation programs in C# codes is provided in a website. A brief description of a general-purpose simulator for executing ACD models is also presented.

"Input uncertainty" refers to the (often unmeasured) effect of not knowing the true, correct distributions of the basic stochastic processes that drive the simulation. These include, for instance, interarrival-time and service-time distributions in queueing models; bed-occupancy distributions in health care models; distributions for the values of underlying assets in financial models; and time-to-failure and time-to-repair distributions in reliability models. When the input distributions are obtained by fitting to observed real-world data, then it is possible to quantify the impact of input uncertainty on the output results. In this tutorial we carefully define input uncertainty, describe various proposals for measuring it, contrast input uncertainty with input sensitivity, and provide and illustrate a practical approach for quantifying overall input uncertainty and the relative contribution of each input model to overall input uncertainty.

Multi-level modeling is concerned with describing a system at different levels of organization and relating their dynamics. ML-Rules is a rule-based language developed for supporting the modeling of cell biological systems. It supports nested rule schemata, the hierarchical dynamic nesting of species, the assignment of attributes and solutions to species at each level, and a flexible definition of reaction rate kinetics. As ML-Rules allows the compact description of rather complex models, means for an efficient execution were developed, e.g., approximate and adaptive algorithms. Experimentation with ML-Rules models is further supported by domain-specific languages for instrumentation and experimentation which have been developed in the context of the modeling and simulation framework JAMES II. A signaling pathway example will illustrate modeling and simulation with ML-Rules.

Cloud computing facilitates access to elastic high performance computing without the associated high cost. Agent-based Modeling & Simulation (ABMS) is being used across many scientific disciplines to study complex adaptive systems. Repast Simphony (Recursive Porous Agent Simulation Toolkit) is a widely used ABMS system. Cloud computing can speed-up significantly ABMS to facilitate more accurate and faster results, timely experimentation, and optimization. However, the many different Clouds, Cloud middleware and Service approaches make the development of Cloud-based ABMS highly complex. This tutorial introduces the CloudSME Simulation Platform (CSSP) that enables simulation software to be deployed as service (SaaS) supported by a cloud platform (PaaS). It shows how Repast can be deployed as a cloud computing service as part of a workflow of tasks. A case study demonstrates how the CSSP can easily run agent-based simulations written in Repast on multiple Clouds.

Understanding the behavior of complex systems is becoming a crucial issue as systems grow in size, and the interconnection and geographical distribution of their components diversifies. The interaction over time of many components often leads to emergent behavior, which can be harmful to the system. Despite this, very few practical approaches for the identification of emergent behavior exist, and many are unfeasible to implement. Approaches using interaction as a measure of emergence have the potential to alleviate this problem. In this paper, we analyse absolute and relative methods that use interaction as a measure of emergence. Absolute methods compute a degree of interaction that characterizes a system state as being emergent. Relative methods compare interaction graphs of the system state with interaction graphs of systems that have been shown previously to exhibit emergence. We present these approaches and discuss their advantages and limitations using theoretical and experimental analysis.

To analyze the impact of intelligent traders with differing fundamental motivations on agent-based simulations of financial markets, we extend the classical zero-intelligence model of financial markets to a positive-intelligence model using the MASON agent-based modeling framework. We exploit multifractal detrended fluctuation analysis (MF-DFA) to analyze the series of stock prices generated by the positive-intelligence simulation. We study the changes in this output process as analyzed by MF-DFA when altering the mix of agents with competing market philosophies; and we compare and contrast the results of fitting conventional time series models to such output processes with the results of applying MF-DFA to the same processes.

Complex scheduling problems require a large amount of computation power and innovative solution methods. The objective of this paper is the conception and implementation of a multi-agent system that is applicable in various problem domains. Independent specialized agents handle small tasks, to reach a superordinate target. Effective coordination is therefore required to achieve productive cooperation. Role models and distributed artificial intelligence are employed to tackle the resulting challenges. We simulate a NP-hard scheduling problem to demonstrate the validity of our approach. In addition to the general agent based framework we propose new simulation-based optimization heuristics to given scheduling problems. Two of the described optimization algorithms are implemented using agents. This paper highlights the advantages of the agent-based approach, like the reduction in layout complexity, improved control of complicated systems, and extendability.

Out-of-shelf events refer to periods of time in which items of a certain product are not available to customers. It is clear that incidents of this nature result in economic loss, but their side effects are much more profound: since there is no record of missed sales opportunities, the estimated demand curve tends to be inaccurate. As a result, order placement strategies employed by retailers are based on imprecise forecast models, so further out-of-shelf events are very likely to occur: a vicious cycle, hence, arises. In this work, we propose a multi-agent-based simulation to evaluate the impact of out-of-shelf events that considers the reactions of customers towards these incidents and retailers’ ordering strategies. Our results show that these events have a significant effect on demand estimation and that multi-agent-based simulations may provide interesting insights and support for the development of more accurate forecast models in retail.

The persistent crisis in Syria has affected millions of its citizens by forcing their displacement from native or accustomed residences. Modeling the Syrian conflict provides a computational means to better understand why, when, and where these citizens flee. Thus, an agent-based model drawn on real-world data to represent Syrian cities (the environment) and the demographic constitution of those cities (the agents) has been developed and is explained in this discussion. The outputs of the model accurately reflect population displacement as it occurred between the years 2011-2012. Importantly, the purpose of this agent-based modeling and the output analysis is to develop a means to anticipate, measure, and assess future displacement in Syria as well as to model other threatened populations in crises where displacement might occur. This paper presents the methodology to crafting the environment and agents to represent the Syrian city of Aleppo and the displacement of its citizens.

Revenue management theory and practice frequently rely on simulation modeling. Simulations are employed to evaluate new methods and algorithms, to support decisions under uncertainty and complexity, and to train revenue management analysts. For all purposes, simulations have to be validated. To enable this, they are calibrated: model parameters are adjusted to create empirically valid results. This paper presents two novel approaches, in which genetic algorithms contribute to calibrating revenue management simulations. The algorithms emulate analyst influences and iteratively adjust demand parameters. In the first case, genetic algorithms directly model analysts, setting influences and learning from the resulting performance. In the second case, a genetic algorithm adjusts demand input parameters, aiming for the best fit between emergent simulation results and empirical revenue management indicators. We present promising numerical results for both approaches. In discussing these results, we also take a broader view on calibrating agent-based simulations.

A novel agent-based model, the Electricity Market Complex Adaptive System (EMCAS) Model, is designed to study market restructuring and the impact of new technologies on the power grid. The agent-based approach captures the complex interactions between the physical infrastructure and the economic behaviors of various agents operating in an electricity market. The electric power system model consists of power generating plants, transmission lines, storage technologies, and load centers. The electric power market is composed of generating company agents who bid capacity and prices into power pools administered by an Independent System Operator (ISO). The ISO agent balances supply and demand for day-ahead markets. EMCAS also simulates real-time market operation to account for the uncertainties in day-ahead forecasts and availability of generating units. This paper describes the model, its implementation, and its use to address questions of congestion management, price forecasting, market rules, and market power.

In the interest of increasing energy efficiency and avoiding higher generation costs during peak periods, utility companies adopt various demand response (DR) methods to achieve load leveling or peak reduction. DR techniques influence consumer behavior via incentives and cause them to shift peak loads to off-peak periods. In this paper we study the energy consumption behavior of residents in response to a variable real-time pricing function. We consider thermostatic loads, specifically air conditioning, as the primary load and apply the model predictive control (MPC) method to study the behavior of consumers who make consumption decision based on a trade-off between energy cost and thermal comfort. An agent-based simulation is used to model a population where each household is an agent embedded with the MPC algorithm. Each household is associated with a multi-attribute utility function, and is uniquely defined via the use of stochastic parameters in the utility function.

Regulators and policy makers, facing a complicated, fast-paced and quickly evolving marketplace, require new tools and decision aides to inform policy. Agent-based models, which are capable of capturing the organization of exchanges, intricacies of market mechanisms, and the heterogeneity of market participants, offer a powerful method for understanding the financial marketplace. To this end, we have worked to develop a flexible and adaptable agent-based model of financial markets that can be extended and applied to interesting policy questions. This paper presents the implementation of this model. In addition, it provides a small case study that demonstrates the possible uses of the model. The source code of the simulation has also been released and is available for use.

We propose an agent-based model to capture the transmission patterns of diseases caused by bioterrorism attacks or epidemic outbreaks and to quickly differentiate between these two scenarios. Focusing on a region of three cities, we want to detect a bioterrorism attack when only a small proportion of the population is infected. Our results indicate that the aggregated infection and death curves in the region can serve as indicators in distinguishing between the two disease scenarios: the slope of the epidemic infection curve will increase initially and decrease afterwards, whereas the slope of the bioterrorism infection curve will strictly decrease. We also conclude that for a bioterrorism outbreak, the dynamic curve from the bioterrorism source city becomes more dominant as the local working probability increases. In contrast, the behavior of individual cities for the epidemic model presents a “time-lag” pattern. As time progresses, the individual city’s dynamic curves converge.

Individuals often use information from broadcast news (e.g. media) and narrowcast news (e.g. personal social network) to form their perception on a certain social issue. Using a case study in social risk amplification, this paper demonstrates that simulation modelling, specifically agent-based simulation, can be useful in analysing the effect of broadcast and narrowcast processes on the formation of public risk perception. The first part of this paper explains the structure of a model that allows easy configuration for testing various behaviours about which the empirical literature cannot make definitive predictions. The second part of this paper discusses the effect of personal social network and the role of media in the dynamics of public risk perception. The results show the undesirable effect of the extreme narrowcast process in society and a media that simply broadcasts the average public risk perception.

Agent-based simulations are indisputably effective for analyzing complex processes such as traffic patterns and social systems. However, human experts often face the challenges in repeating the simulation many times when evaluating a large variety of scenarios. To reduce the computational burden, we propose an approach for inferring the end results in the middle of simulations. For each simulated scenario, we design a feature that compactly aggregates the agents’ states over time. Given a sufficient number of such features we show how to accurately predict the end results without fully performing the simulations. Our experiments with traffic simulations confirmed that our approach achieved better accuracies than existing simulation metamodeling approaches that only use the inputs and outputs of the simulations. Our results imply that one can quickly evaluate all scenarios by performing full simulations on only a fraction of them, and partial simulations on the rest.

The goal of this paper is to analyze drivers’ en-route divergence behaviors when a road way is blocked by a car incident. The Extended Belief-Desire-Intention (E-BDI) framework is adopted in this work to mimic real drivers’ uncertain en-route planning behaviors based on the drivers’ perceptions and experiences. The proposed approach is implemented in Java-based E-BDI modules and DynusT® traffic simulation software, where a traffic data of Phoenix in the U.S. is used to illustrate and demonstrate the proposed approach. For validation of the proposed approach, we compare the drivers’ en-route divergence patterns obtained by E-BDI en-route planning with the divergence patterns provided by Time Dependent Shortest Path (TDSP) finding algorithm of DynusT®. The results have revealed that the proposed approach allows us to better understand various divergence patterns of drivers so that a reliable traffic system considering impacts of the sudden road way blocking events can be designed.

Cloud-based M&S can have many forms, from hardware as a service or cloud-based data for M&S applications to providing M&S as a service. In order to be able to compose such cloud-based M&S services, these services not only need to be able to exchange data and use such exchanged data, they also must represent truth consistently. Current paradigms are not sufficient to support these requirements. In this paper, a new paradigm is proposed that uses mobile propertied concepts to support consistent simulations using composable cloud-based M&S services. Mobile Propertied Agents (MPA) will utilize a floating middleware to establish an event-cloud orchestrated by a truth control layer. The result is a flexible Event Service Bus that ensure the consistent representation of truth in all systems connected to the event cloud, thus ensuring interoperability and composability by design in this cloud-based M&S environment.

Performance availability is an approach to rate the performance of material flow systems. Since the data necessary to determine the performance availability can only be obtained by observing the system in operation, planning towards a certain performance availability is a challenging task. In this paper, we present an approach to model an AGV (Automated Guided Vehicle) based logistics facility to ultimately measure and visualize the performance availability of the system within VR environments. We employed 3-D laser scans to create a visual representation of the facility and modelled the mechanical components using the simulation system’s kinematic mechanisms. An interface to the real system’s control architecture makes it possible to incorporate real world data and scenarios. Data not readily visible or not visible at all such as vehicle health, waiting times, and running times is surveyed and presented in a comprehensive VR environment for evaluating overall system performance and performance availability.

We propose a novel simulation-based optimization method to solve the biorefinery location problem in competitive corn markets. As the feedstock cost is the largest cost component for producing ethanol, it is critical to consider the formation of corn prices in the local markets around biorefineries. The corn prices are determined by competition among biorefineries, among farmers, between biorefineries and the food market. However, the competition is often ignored in previous studies. In this work, we formulate the competition in the biorefinery location problem by agent-based modelling and simulation of the local corn markets. The corn prices are determined by double auction mechanism in which the biorefineries and the food market are buyers and the farmers are the sellers. The determined prices are then imported to the optimization problem, which is solved by a genetic algorithm. The proposed method is demonstrated by a case study on biorefinery locations in Illinois.

Inventory optimization is critical in supply chain management. The complexity of real-world multi-echelon inventory systems under uncertainties results in a challenging optimization problem. We propose a novel simulation-based optimization framework for optimizing distribution inventory systems where each facility is operated with the (r, Q) inventory policy. The objective is to minimize the inventory cost while maintaining acceptable service levels quantified by the fill rates. The inventory system is modeled and simulated by an agent-based system, which returns the performance functions. The expectations of these functions are then estimated by the Monte-Carlo method. Then the optimization problem is solved by a cutting plane algorithm. As the black-box functions returned by the Monte-Carlo method contain noises, statistical hypothesis tests are conducted in the iteration. A local optimal solution is obtained if it passes the test on the optimality conditions.

Safety planning for crowd evacuation is an important and active research topic nowadays. One important issue is to devise the evacuation plans of individuals in emergency situations so as to reduce the total evacuation time. This paper proposes a novel evolutionary algorithm (EA)-based methodology, together with agent-based crowd simulation, to solve the evacuation planning problem. The proposed method features a novel segmentation strategy which divides the entire evacuation region into sub-regions based on a discriminant function. Each sub-region is assigned with an exit gate, and individuals in a sub-region will run toward the corresponding exit gate for evacuation. In this way, the evacuation planning problem is converted to a symbolic regression problem. Then an evolutionary algorithm, using agent-based crowd simulation as fitness function, is developed to search for the global optimal solution. The simulation results on different scenarios demonstrate that the proposed method is effective to reduce the evacuation time.

Crowdsourcing is a complex system composed of many interactive distributed agents whom we have little information about. Agent-based modeling (ABM) is a natural way to study complex systems since they share common properties, such as the global behavior emerging on the basis of local interactions between elements. Although significant attention has been given to dynamics of crowdsourcing systems, relatively little is known about how workers react to varying configurations of tasks. In addition, existing ABMs for crowdsourcing systems are theoretical, and not based on data from real crowdsourcing platforms. The focus of this paper is on capturing the relationships among properties of tasks, characteristics of workers, and performance metrics via an ABM. This approach is validated by running experiments on Amazon Mechanical Turk (AMT).

This article proposes a generic agent-supported symbiotic simulation architecture that generates and evaluates competing coherence-driven workflows using genetic programming. Workflows are examined by an agent-supported multi-simulation environment that allows inducing variation and assessing the outcome of the candidate workflows under a variety of environmental scenarios. The evaluation strategy builds on a coherence-driven selection mechanism that views assessment as a constraint satisfaction problem. The proposed system is also based on an introspective architecture that facilitates monitoring the activities of competing agent simulations as well as the status of the environment to determine the success of the candidate workflow with respect to given or emergent goals.

The operation of transmission grids in power markets is complex because loads and generation need to be continuously balanced and because unexpected changes in supply and demand make electricity prices volatile. Congestion of transmission and the need to allocate congested capacity is one of the problems that arise in this operation. Congestion can be managed with mechanisms based on rules and also with market mechanisms such as financial transmission rights. In this paper we present a simulation model for evaluating options for managing transmission capacity in the Colombian electricity system. The model combines agent-based simulation with optimization in order to examine how allocating and pricing congestion capacity using financial transmission rights affects electricity supply and prices. Results from the model suggest that a market approach is effective in managing transmission congestion, although it increases the complexity of market rules.

An improved variance reduction method for accurate estimation of the price, delta, and gamma of Asian options in a single simulation is presented. It combines randomized quasi-Monte Carlo with very efficient new control variates, that are especially successful in reducing the variance of the pathwise derivative method used to simulate delta and gamma. To improve the performance of randomized quasi-Monte Carlo, we smooth the integrands by employing conditional Monte Carlo and reduce the effective dimension of the smoothed integrands by using principal component analysis. Numerical results show that the new method yields significant variance reduction for the price, for delta and for gamma.

Drawing upon the work of Ghamami and Zhang [2013], this paper presents an efficient Monte Carlo framework for the estimation of credit value adjustment (CVA), one of the most widely used and regulatory-driven counterparty credit risk measures. Our proposed efficient CVA estimators are developed based on novel applications of well-known mean square error (MSE) reduction techniques in the simulation literature. Our numerical examples illustrate that the efficient estimators outperform the existing crude estimators of CVA substantially in terms of MSE.

The square-root process is used to model interest rates and volatility in financial mathematics. The pricing of derivatives involving that process often requires simulating it, since there are often no explicit formulas for prices. We study how a change of measure (CM) may improve those simulations. We compare with Andersen’s quadratic-exponential scheme (QE), which so far appears to be the most efficient technique to simulation the stochastic differential equation satisfied by the square-root process. An integer-dimension squared Bessel process, easy to simulate, is used to generate the law of the square-root process using a change of measure. The new method performs very well, and the two algorithms execute at similar speeds; however, CM is slower than QE if random number generation is taken into account, because CM requires more random numbers. The Radon-Nikodym derivative sometimes has a rather intriguing behavior, which is itself of interest. We propose an explanation.

The Poisson process has been an integral part of many models for the arrival process to a telephone call centers. However, several publications in recent years suggest the presence of a significant "overdisperson'' relative to the Poisson process in real-life call center arrival data. In this paper, we study the overdispersion in the context of ``heavy traffic'' and identify a critical factor that characterizes the stochastic variability of the arrivals to their averages. We refer to such a factor as the scaling parameter and it potentially has a profound impact on the design of staffing rules. We propose an new model to capture the scaling parameter in this paper.

Even when they are known to be continuous, Poisson-process rate functions are sometimes specified as piecewise constant. To better approximate the unknown continuous rate function, we fit a piecewise-quadratic function. In addition to maintaining the rate's integral over each time interval, at each interval's end point we match the rates and their first derivatives. For every interval with negative rates, we force non-negativity by taking the maximum of zero and the quadratic-function value, modifying the quadratic to keep the original integral value. These rate functions can be used alone or applied after one or more iterations of I-SMOOTH, an existing algorithm (by the first two authors) designed for the same problem. We briefly discuss random-process generation from the new rate functions. Finally, we provide examples.

We consider the problem of using observations of a counting process measured multiple times over a period to create an intensity function for a Non-Homogeneous Poisson Process (NHPP) that estimates the observed process, is consistent with the data, is continuous, and is "smooth". Optionally, we may also require the intensity value at the end of the period be identical to the intensity value at the beginning of the period, for application in contexts where the period of interest is inherently cyclic, e.g., a day, or a week.

Our approach is to define a class of continuous piecewise linear intensity functions and formulate the problem as a constrained quadratic programming problem whose solution is obtained through the solution of a simultaneous set of linear equations. We describe the approach, identify conditions under which feasible solutions are assured to exist, and study the behavior of the solutions on some example problems.

Spatial statistical models are of considerable practical and theoretical interest. However, there has been little work on rare-event probability estimation for such models. In this paper we present a conditional Monte Carlo algorithm for the estimation of the probability that random graphs related to Bernoulli and continuum percolation are connected. Numerical results are presented showing that the conditional Monte Carlo estimators significantly outperform the crude simulation estimators.

A key challenge for an efficient splitting technique is defining the importance function. If the rare event set consists of multiple separated subsets this challenge becomes bigger since the most likely path to the rare event set may be very different from the most likely path to an intermediate level. We propose to mitigate this problem of path deviation by estimating the subset probabilities separately using a modified splitting technique. We compare the proposed separated splitting technique with a standard splitting technique by estimating the probability of entering either of two separated intervals on the real line. The squared relative error of the estimator is shown to be significantly higher when using standard splitting than when using separated splitting. We show that this difference increases if the rare event probability becomes smaller, illustrating the advantage of the separated splitting technique.

In this paper, we consider rare-event simulation of the tail probabilities of Gaussian random fields. In particular, we design importance sampling estimators that are uniformly efficient for a family of Gaussian random fields with different mean and variance functions.

We consider the problem of estimating the unreliability of a stochastic flow network, defined as the probability that the maximum flow value from a source node to a terminal node in a directed network with stochastic link capacities, is less than a specified demand level. The link capacities are assumed to be continuous random variables with a known joint distribution. We are interested in the situation where the unreliability is very small, in which case a crude Monte Carlo is not viable. We show how a Monte Carlo splitting algorithm can be adapted to handle this problem effectively.

This paper describes how importance sampling can be applied to efficiently estimate the average interval availability of highly reliable Markovian systems, made of components subject to failures and repairs. We describe a methodology for approximating the zero-variance change of measure. The method is illustrated to be very efficient on a small example, compared with standard importance sampling strategies developed in the literature.

In this paper, we construct efficient importance sampling Monte Carlo schemes for finite time exit probabilities in the presence of rest points. We focus on reversible diffusion processes with small noise that have an asymptotically stable equilibrium point. The main novelty of the work is the inclusion of rest points in the domain of interest. We motivate the construction of schemes that perform well both asymptotically and non-asymptotically. We concentrate on the regime where the noise is small and the time horizon is large. Examples and simulation results are provided.

We develop an algorithm that generates samples from a given probability distribution on a manifold embedded in a Euclidean space based only on the ability to evaluate the mapping defined by the parametrization of the manifold. In particular, we do not assume the ability to evaluate the derivatives of the mapping and the ability to tell whether a given point in the ambient space belongs to the manifold or not. The new approach is useful when the manifold is analytically intractable and highly nonlinear---for example, in studying complex regulatory networks in systems biology where the mapping is typically defined by the solution of a system of ordinary differential equations.

In this paper, we develop a new simulation algorithm that generates unbiased gradient estimators for the steady-state workload of a stochastic fluid network, with respect to the throughput rate of each server. Our algorithm is based on a recently developed perfect sampling algorithm, and the infinitesimal perturbation analysis (IPA) method. We illustrate the performance of our algorithm with several multidimensional examples, including its formal application in the case of multidimensional Brownian motion

We consider a common type of robust performance analysis that is formulated as maximizing an expectation among all probability models that are within some tolerance of a baseline model in the Kullback-Leibler sense. The solution of such concave program is tractable and provides an upper bound which is robust to model misspecification. However, this robust formulation fails to preserve some natural stochastic structures, such as i.i.d.~model assumptions, and as a consequence, the upper bounds might be pessimistic. Unfortunately, the introduction of i.i.d.~assumptions as constraints renders the underlying optimization problem very challenging to solve. We illustrate these phenomena in the rare event setting, and propose a large-deviations based approach for solving this challenging problem in an asymptotic sense for a natural class of random walk problems.

A detailed simulation model is presented with the purpose of analyzing the congestion and interaction between bus lines and passengers at stops. Our main goal is to perform a complete validation of a simulation model formalized in a standard language in order to use it as a basis to perform more complex experiments. The basis of the model is a queuing model that leads us to perform an operational validation. Since the model is completely represented using a formal language, the specialist can perform a formal validation of the model previously to any implementation. Thanks to the modular structure of the formal language used to define the model, the model can be easily expand to represent more complex systems. Due to a formal representation, the implementation process can be done automatically implying that analysts should only be concerned about the correct definition of the diagrams that represents the model behavior.

Simulation has been widely used in modeling engineering systems. A metamodel is a surrogate model used to approximate a computationally expensive simulation model. Extensive research has investigated the performance of different metamodeling techniques in terms of accuracy and/or robustness and concluded no model outperforms others across diverse problem structures. Motivated by this finding, this research proposes a bi-criteria (accuracy and robustness) optimized ensemble framework to optimally identify the contributions from each metamodel (Kriging, Support Vector Regression and Radial Basis Function), where uncertainties are modeled for evaluating robustness. Twenty-eight functions from the literature are tested. It is observed for most problems, a Pareto Frontier is obtained, while for some problems only a single point is obtained. Seven geometrical and statistical metrics are introduced to explore the relationships between the function properties and the ensemble models. It is concluded that the bi-criteria optimized ensembles render not only accurate but also robust metamodels.

Simulation model validation and its rigorous assessment is known to be a difficult task. Quantification of validation is necessary to answer the question 'how much validation is adequate?' One can answer this question by developing adequacy criteria to measure the validation performed on a simulation model. Developing test adequacy criteria for verifying computer programs has been useful for improving the quality and increasing confidence of regular software systems. We argue that from the validation and verification (V&V) perspective, simulation models are no different than software that are generally termed as `non-testable' due to the absence of a test oracle. There has been little research to develop V&V related adequacy criteria for these types of programs. We present a validation coverage criterion, show in detail how it applies on discrete event simulation (DES) models, and discuss how it can be extended towards developing V&V coverage criteria for other `non-testable' programs.

Quantiles are often used in risk evaluation of complex systems. In some situations, as in safety analyses of nuclear power plants, a confidence interval is required for the quantile of the simulation's output variable. In our current paper, we develop methods to construct confidence intervals for quantiles when applying Latin hypercube sampling, a variance reduction technique that extends stratification for sampling in higher dimensions. Our approaches employ the batching and sectioning methods when applying replicated Latin hypercube sampling, with a single Latin hypercube sample in each batch, and samples across batches are independent. We have established the asymptotic validity of the confidence intervals developed in this paper. Moreover, we have proven that quantile estimators from a single Latin hypercube sample and replicated Latin hypercube samples satisfy weak Bahadur representations. An advantage of sectioning over batching is that the sectioning CI typically has better coverage, which we observe in numerical experiments.

We analyze the convergence to equilibrium of one-dimensional reflected Brownian motion (RBM) and compute a number of related initial transient formulae. These formulae are of interest as approximations to the initial transient for queueing systems in heavy traffic, and help us to identify settings in which initialization bias is significant. We conclude with a discussion of mean square error for RBM. Our analysis supports the view that initial transient effects for RBM and related models are typically of modest size relative to the intrinsic stochastic variability, unless one chooses an especially poor initialization.

Sequest is a fully sequential procedure that delivers improved point and confidence-interval (CI) estimators for a designated steady-state quantile by exploiting a combination of ideas from batching and sectioning. Sequest incorporates effective methods to do the following: (a) eliminate bias in the sectioning-based point estimator that is caused by initialization of the simulation or an inadequate simulation run length (sample size); and (b) adjust the CI half-length for the effects of skewness or correlation in the batching-based point estimators of the designated quantile. Sequest delivers a CI designed to satisfy user-specified requirements concerning both the CI's coverage probability and its absolute or relative precision. We found that Sequest exhibited good small- and large-sample properties in a preliminary evaluation of the procedure's performance on a suite of test problems that includes some problems designed to ``stress test'' the procedure.

When we use simulation to estimate the performance of a stochastic system, lack of fidelity in the random input models can lead to poor system performance estimates. Since the components of many complex systems could be dependent, we want to build input models that faithfully capture such key properties. In this paper, we use the flexible NORmal To Anything (NORTA) representation for dependent inputs. However, to use the NORTA representation we need to estimate the marginal distribution parameters and a correlation matrix from real-world data, introducing input uncertainty. To quantify this uncertainty, we employ the bootstrap to capture the parameter estimation error and an equation-based stochastic kriging metamodel to propagate the input uncertainty to the output mean. Asymptotic analysis provides theoretical support for our approach, while an empirical study demonstrates that it has good finite-sample performance.

For system dynamics simulation (SD) models, an estimation of statistical distributions for uncertain parameters is crucial. These distributions could be used for testing models sensitivity, quality of policies, and/or estimating confidence intervals for these parameters. Assumptions related to normality, independence and constant variation are often misapplied in dynamic simulation. Bootstrapping holds a considerable theoretical advantage when used with non-Gaussian data for estimating empirical distributions for unknown parameters. Although it is a widely acceptable approach, it has had only limited use in system dynamics applications. This paper introduces an application of Direct Residual Bootstrapping (DRBS) for statistical inference in system dynamic model. DRBS has been applied successfully to ‘The Irish Elderly Patient Delayed Discharge’ dynamic model to estimate empirical distribution for some unknown parameters with a minimal computation effort. The computational results show that bootstrapping offers an efficient performance in cases of no availability of prior information of model parameters.

In some service operations settings, data are available only for system outputs but not the constituent input models. Examples are service call centers and patient flows in clinics, where sometimes only the waiting time or the queue length data are collected for economic or operational reasons, and the data on the "input distributions", namely interarrival and service times, are limited or unavailable. In this paper, we study the problem of estimating these input distributions with only the availability of the output data, a problem usually known as the inverse problem, and we are interested in the context where stochastic simulation is required to generate the outputs. We take a nonparametric viewpoint, and formulate this inverse problem as a stochastic program by maximizing the entropy of the input distribution subject to moment matching. We then propose an iterative scheme via simulation to approximately solve the program.

When using simulations for decision making, no matter the domain, the uncertainty of the simulations' output is an important concern. This uncertainty is traditionally estimated by propagating input uncertainties forward through the simulation model. However, this approach requires extensive data collection before the output uncertainty can be estimated. In the worst case scenario, the output may even prove too uncertain to be usable, possibly requiring multiple revisions of the data collection step. To reduce this expensive process, we propose a method for inverse uncertainty propagation using Gaussian processes. For a given bound on the output uncertainty, we estimate the input uncertainties that minimize the cost of data collection and satisfy said bound. That way, uncertainty requirements for the simulation output can be used for demand driven data acquisition. We evaluate the efficiency and accuracy of our approach with several examples.

Prior to making a multiple attribute selection decision, a decision-maker may collect information to estimate the value of each attribute for each alternative. In this work, we consider a fixed experimental sample budget and address the problem of how best to allocate this budget across three attributes when the attribute value estimates have a normally distributed measurement error. We illustrate that the allocation choice impacts the decision-maker’s ability to select the true best alternative. Through a simulation study we evaluate the performance of a common allocation approach of uniformly distributing the sample budget across the three attributes. We compare these results to the performance of several allocation rules that leverage the decision-maker’s preferences. We found that incorporating the decision-maker’s preferences into the allocation choice improves the probability of selecting the true best alternative.

Effective uncertainty evaluation is a critical step toward real-time and robust decision-making for complex systems in uncertain environments. A Multivariate Probabilistic Collocation Method (M-PCM) was developed to effectively evaluate system uncertainty. The method smartly chooses a limited number of simulations to produce a low-order mapping, which precisely predicts the mean output of the original mapping. While the M-PCM significantly reduces the number of simulations, it does not scale with the number of uncertain parameters, making it difficult to use for large-scale applications. In this paper, we develop a method to break the curse of dimensionality. The method integrates M-PCM and Orthogonal Fractional Factorial Design (OFFD) to reduce the number of simulations. The integrated M-PCM-OFFD predicts the correct mean of the original mapping, and is the most robust to numerical errors among all designs of the same number of simulations. The analysis also provides new insights on the optimality of OFFDs.

This paper considers the factor screening problem with multiple responses for simulation experiments. The objective is to identify critical factors with controlling Family-Wise Error Rate. The metamodel of interest is the first order linear model. Factors are independent with each others, and responses are from multivariate normal distribution. Two procedures, Sum Intersection Procedure (SUMIP) and Sort Intersection Procedure (SORTIP) are proposed and verified. Numerical studies are provided to demonstrate the validity and efficiency of our proposed procedures.

Often the accurate estimation of multiple values from a single simulation is of practical importance. Among the many variance reduction methods known in the literature, stratified sampling is especially useful for such a task as the allocation fractions can be used as decision variables to minimize the overall error of all estimates. Two different classes of overall error functions are proposed. The first, including the mean squared absolute and the mean squared relative error, allows for a simple closed-form solution. For the second class of error functions, including the maximal absolute and the maximal relative error, a simple and fast heuristic is proposed. The application of the new method, called “multiresponse stratified sampling”, and its performance are demonstrated with numerical examples.

In this paper we describe a highly scalable multi-modal traffic simulation platform on parallel and distributed environments ranging from multi-core or many core machines to multi-node clusters or supercomputer environments. We evaluated our platform with multi-modal transportation networks from the capital city of Ireland, Dublin, and verified its high scalability and real-time performance on both a 1-node with 12 core machine for a multi-threading environment and a cluster of 12 nodes (with a total of 144 cores) for a distributed system. With this platform, municipal planners or traffic engineers at transportation companies can quickly conduct many series of simulations with various what-if scenarios in a parallel and distributed manner, allowing them to assess and select the best responses to sudden incidents, or plan for major events or disaster responses.

Many social simulations can be represented using mobile-agent-based model in which agents moving around on a given space such as evacuations, traffic flow and epidemics. Whole planet simulation with billions of agents at microscopic level helps mitigate the global crisis. It introduces new technical challenges such as processing and migrating many agents and load balancing among hundreds of machines. To overcome these challenges, well-designed software architecture of a simulator is essential. In this research, we proposed agent-based complex cellular automata architecture (ABCCA) and studied the performance and scalability of two cell-based processing models, through simple traffic flow simulation on multi-core distributed system. The experiments show that the computation speedup can be achieved by reducing granularity of tasks and processing only active spaces. We achieved running the traffic flow simulation with one billion of agents in almost real time on 1,536 CPU cores of total 128 machines of TSUBAME supercomputer.

This paper proposes using intermediate features of traffic simulations in a genetic algorithm designed to find the best scenarios in regulating traffic with multiple objectives. A challenge in genetic algorithms for multi-objective optimization is how to find various optimal scenarios within a limited decision time. Typical evolutionary algorithms usually maintain a population of diversified scenarios whose diversity is measured only by the final objectives available at the end of their simulations. We propose measuring the diversity by also the time series of the objectives during the simulations. The intuition is that simulation scenarios with similar final objective values may contain different series of discrete events that, when combined, can result in better scenarios. We provide empirical evidence by experimenting with agent-based traffic simulations showing the superiority of the proposed genetic algorithm over standard approaches in approximating Pareto fronts.

Data mining tools have been around for several decades, but the term "big data" has only recently captured widespread attention. Numerous success stories have been promulgated, as organizations have sifted through massive volumes of data to find interesting patterns that are, in turn, transformed into actionable information. Yet a key drawback to this big data paradigm is that it relies on observational data - limiting the types of insights that can be gained. The simulation world is different. A "data farming" metaphor captures the notion of purposeful data generation from simulation models. Large-scale designed experiments let us grow the simulation output efficiently and effectively. We can explore massive input spaces, uncover interesting features of complex simulation response surfaces, and explicitly identify cause-and-effect relationships. With this new mindset, we can achieve quantum leaps in the breadth, depth, and timeliness of the insights yielded by simulation models.

Stochastic composite simulation models, such as those created via the IBM Splash prototype platform, can be used to estimate performance measures for complex stochastic systems of systems. When, as in Splash, a composite model is made up of loosely coupled component models, we propose a method for improving the efficiency of composite-model simulations. To run N Monte Carlo replications of the composite model, we execute certain component models fewer than N times, caching and re-using results as needed. The number of component-model replications is chosen to maximize an asymptotic efficiency measure that balances computation costs and estimator precision. In this paper we initiate the study of result-caching schemes by giving an exact theoretical analysis for the most basic two-model scenario, as well as outlining some approaches for obtaining the parameter values needed for result caching.

Public health agencies traditionally rely heavily on epidemiological reporting for notifiable disease control, but increasingly apply simulation models for forecasting and to understand intervention tradeoffs. Unfortunately, such models traditionally lack capacity to easily incorporate information from epidemiological data feeds. Here, we introduce particle filtering and demonstrate how this approach can be used to readily incorporate recurrently available new data so as to robustly tolerate – and correct for – both model limitations and noisy data, and to aid in parameter estimation, while imposing far less onerous assumptions regarding the mathematical framework and epidemiological and measurement processes than other proposed solutions. By comparing against synthetic ground truth produced by an agent-based model, we demonstrate the benefits conferred by particle filtering parameters and state variables even in the context of an aggregate, incomplete and systematically biased compartmental model, and note important avenues for future work to make such approaches more widely accessible.

High resolution population distribution data is critical for successfully addressing critical issues ranging from energy and socio-environmental research to public health to homeland security. Commonly available population data from Census is constrained both in space and time and does not capture the population dynamics as functions of space and time. This imposes a significant limitation on the fidelity of event based simulation models with sensitive space-time resolution. This paper will describe ongoing development of high-resolution population distribution and dynamics models, at Oak Ridge National Laboratory, through spatial data integration and modeling with behavioral or activity-based mobility datasets for representing temporal dynamics of population. The model is resolved at 1 km resolution globally and describes the U.S. population for nighttime and daytime at 90m. In addition, we will discuss development and integration of transportation, physical and behavioral science computational approaches for applications in critical infrastructure management from local to global scales.

There is an increasing interest on using Online Social Networks (OSNs) in a wide range of applications. Two interesting problems that have received a lot of attention in OSNs is how to provide effective ways to understand and predict how users behave, and how to build accurate models for specific domains (e.g. marketing campaigns). In this context, stochastic multi-agent based simulation can be employed to reproduce the behavior observed in OSNs. Nevertheless, the first step to build an accurate behavior model is to create an agent-based system. Hence, a modeler needs not only to be effective, but also to scale up given the huge volume of streaming graph data. To tackle the above challenges, this paper proposes a MapReduce-based method to build a modeler to handle big data. By considering the Obama's Twitter network on the presidential campaign, we show experiments on how efficient and effective our proposal is.

The Financial Stability Oversight Council (FSOC) was created to identify and respond to emerging threats to the stability of the U.S. financial system. The research arm of the FSOC, the Office of Financial Research, has begun to explore agent-based models (ABMs) for measuring the emergent threat of systemic risk. We propose an ABM-based regulatory structure that incentivizes the honest participation and data contribution of regulated firms while providing clarity into the actions of the firms as endogenous to the market and driving emergent behavior. We build this scheme onto an existing ABM of a single-asset market to examine whether the structure of this scheme could provide its own benefits to market stabilization. We find that without regulatory intervention, markets acting within this proposed structure experience fewer bankruptcies and lower leverage buildup while returning larger profits for the same amount of risk.

High Performance Computing is becoming an instrument in its own right. The largest simulations per¬for¬med on our supercomputers are now approaching petabytes, and at Exascale everything becomes a Big Data problem. As the volume of these simulations is growing, it is becoming harder to access, analyze and visualize these data. At the same time for a broad community buy-in we need to provide public access to the simulation results. This is only possible if the analyses and visualizations are co-located with the data. We discuss the concept of open numerical laboratories providing a public, interactive access to petascale simulations for the broad science community.

Development of an effective data analytics application for manufacturing requires testing with large sets of data. It is usually difficult for application developers to find access to real manufacturing data streams for testing new data analytics applications. Virtual factories can be developed to generate the data for selected measures in formats matching those of real factories. The vision of a virtual factory has been around for more than a couple decades. Advances in technologies for computation, communication, and integration and in associated standards have made the vision of a virtual factory within reach now. This paper discusses requirements for a virtual factory to meet the needs of manufacturing data analytics applications. A framework for the virtual factory is proposed that leverages current technology and standards to help identify the developments needed for the realization of virtual factories.

This paper investigates the impact of the dependence parameter uncertainty on data-driven decision making. When the dependence parameters are known, ignoring the dependencies in system inputs wastes significant resources in production and service systems. In the case of unknown dependence parameters, the assumption of an independent input process to minimize the expected cost of input parameter uncertainty becomes preferable to accounting for the dependence parameter uncertainty in certain cases. Therefore, a fundamental question to answer before capturing the dependence parameter uncertainty in a stochastic system simulation is whether there is sufficient statistical evidence to represent the dependence in the presence of limited data. We seek an answer for this question within a data-driven inventory-management context, propose two new finite-sample hypothesis tests to investigate the existence of this correlation, and illustrate them with examples.

This paper presents preliminary analysis of the Panama Canal Expansion from the viewpoint of salinity in the Gatun Lake and the utilization of neural networks. This analysis utilized simulation modeling and artificial intelligence. We have built several discrete and system dynamics simulation models of the current Panama Canal operations and the future expansion which have been validated with historical and projected data and Turing/expert validation by engineers of the Panama Canal Authority. The simulation models have been exercised in order to generate enough information about the future expansion. This information has been used to develop neural networks that have the capability to indicate the volume of the Gatun Lake and its respective salinity taking into consideration lockages, spillovers, hydropower generation, fresh water supply volumes, and environmental factors such as precipitation, tides, and evaporation. Support vector machines were used to build time series regression models of the evaporation of Gatun Lake.

To resolve high-performance content-based event matching problem for large-scale publish/subscribe systems, we have focused on how to use some priori knowledge to improve the efficiency. In this paper, by theoretically analyzing the inherent problem of the matching order of predicates, we propose a matching algorithm called Match-Ladder which based on the best matching order. The Match-Ladder can achieve better trade-off between time efficiency and the usage of memory space. It has been verified through both mathematical and simulation-based evaluation.

This paper describes a tool for exploring system scheduler parameter settings in a heterogeneous computing environment. Through the coupling of simulation and optimization techniques, this work investigates optimal scheduling intervals, the impact of job arrival prediction on scheduling, as well as how to best apply fair use policies. The developed simulation framework is quick and modular, enabling decision makers to further explore decisions in real-time regarding scheduling policies or parameter changes.

Computerized decision making is becoming a reality with exponentially growing data and machine capabilities. Some decision making is extremely complex, historically reserved for governing bodies or market places where the collective human experience and intelligence come to play. Other decision making can be trusted to computers that are on a path now into the future through novel software development and technological improvements in data access. In all cases, we should think about this carefully first: what data is really important for our goals and what data should be ignored or not even stored? The answer to these questions involves human intelligence and understanding before the data-to-decision process begins.

The IDEF-SIM technique was developed in order to facilitate the translation of conceptual models to computational models. Papers using this technique can be found in the existing literature; however, none present an analysis of how the technique behaves throughout all of the stages of a discrete event simulation project. Therefore, a research-action was developed with the practical objective of constructing a computational model as a help to future decision making. The knowledge objective is to analyze the applicability of this technique in all of the stages of the discrete event simulation project. It was concluded that the IDEF-SIM demonstrates as applicable: during the conception stage in which information of the process was documented in detail; during the implementation stage in which the conceptual model was converted into a computational model and it; lastly, during the analysis phase, promoting the communication between modulators and managers in the presentation of proposed changes.

Dollar Cost Averaging is a periodic investment of equal dollar amounts in stocks which allegedly can reduce (but not avoid) the risks of security investment. Even if some academic contributions questioned the alleged benefits, several professional investment advisors and websites keep on suggesting it. In this paper we use simulation to analyze Dollar Cost Averaging performance and compare its results to Lump Sum investment. We consider $30$ international funds and $30$ stocks to simulate investing over different period windows in order to assess whether this strategy is better than investing the whole available sum at time $0$.

Our goal is to support capacity management for systems such as hospitals, campuses, and cities, which utilize resources such as people, places, and things in complex ways. Simulation tools have traditionally been used for these sorts of studies, but they require expert model builders to create and maintain abstract business process models of the system under study. This can lead to a lack of representativeness and difficulty in adapting the model for additional or different study scenarios. This paper presents a new simulation approach, Simulation By Example, which overcomes these problems by guiding the simulation using traces, i.e., examples, of the behavior of the actual system but without requiring explicit business process models to be authored. Instead we rely on system instrumentation to capture the traces. We demonstrate the method in two case studies for healthcare systems as described in recent literature.

This paper examines how setting targets in organizations affects decision making. We assume a division acts to maximize the probability of meeting its given target. We use a simulation-based model to quantify the value gap that results from this target-based behavior in relation to utility maximizing behavior. We define an optimal target as one that minimizes the value gap. We investigate the effects of the organization’s risk aversion, the number of potential decision alternatives, and the distribution of the alternatives on both the value gap and the optimal target. The distribution of the alternatives is modeled with a copula based method. The results show that the optimal target (i) decreases as the risk aversion increases; (ii) increases as the number of available alternatives increase; and (iii) decreases as the alternatives approach some efficient frontier. We discuss the rationale and implications for the simulation results.

Context aware workflows are adapted to changing circumstances to meet their execution performance requirements. Adaptation can be performed reactively or proactively. Predictive or runtime simulation can be used to adapt workflows proactively. This paper proposes an approach for using the predictive simulation in improving efficiency of customer service workflows. The predictive simulation is invoked during the workflow execution to evaluate expected workflow performance in the current context and to adapt workflow execution accordingly. Efficiency of the predictive simulation is evaluated experimentally using an example of the digital service design at the museum. It is shown on the basis of simulation results that the proactive adaptation is more efficient than the reactive adaptation, especially, in the case of high visitor flow.

Supplier risk jeopardizes on-time or full-amount raw material delivery of a supply chain. Traditionally, an enterprise can merely evaluate a supplier’s performance based on historic data and performs in a reactive rather than a proactive way in emergency. We propose an agile process management framework to monitor and manage supply risk. The innovation is two fold - Firstly, a business process is in place to make sure that the right data, the right insights, and the right decision-makers are there in the right time. Secondly, we install a big data analytics component, a simulation component and an optimization component into the business process. The big data analytics component senses and predicts supply disruptions with internally (operational) and external (environmental) data. The simulation component supports risk evaluation to convert predicted risk severity to key performance indices (KPI) such as cost and stockout percentage. The optimization component assists the risk-hedging decision-making.

Technical debt is a well understood yet understudied phenomena. A current issue is the verification and validation of proposed methods for technical debt management in the context of agile development. In practice, such evaluations are either too costly or too time consuming to be conducted using traditional empirical methods. In this paper, we describe a set of simulations based on models of the agile development process, Scrum, and the integration of technical debt management. The purpose of this study is to identify which strategy is superior and to provide empirical evidence to support existing claims. The models presented are based upon conceptual and industry models concerning defects and technical debt. The results of the simulations provide compelling evidence for current technical debt management strategies proposed in the literature that can be immediately applied by practitioners.

The demand for higher product availability has increased through product and service offerings such as Product Service Systems (PSS), where the product is sold for its use rather than the product itself. This has led to pressures on maintenance operations, particularly for out of sight products. Some authors have suggested applying sensors and the use of diagnostics and prognostics to monitor product performance driven by the generally held belief that diagnosing and/or predicting future failure will lead to higher product availability. In this paper, we show the ability of Discrete Event Simulation (DES) to compare between different product monitoring levels. This capability is then applied to an industrial case to investigate whether or not the higher the monitoring level leads to higher product availability.

Conceptual Modeling (CM) has gained a lot of interest in the recent years and it is widely agreed that CM is the most important phase of simulation study. Despite its significance, there are very few techniques that can help to develop well-structured and concise conceptual models. This paper proposes the use of the Structured Analysis and Design Technique (SADT) from software engineering to develop conceptual models. SADT has proven to be successful in the development of software systems, specifically in the requirements gathering phase. This paper contributes to the area of CM by proposing a new framework for developing conceptual models which focuses mainly on the first phase of CM, that of System Description (SD). A simple case, the Panorama Televisions production plant, is used to illustrate the application of this approach. The benefits and limitations of this framework are discussed.

Hybrid microgrids containing renewable energy sources represent a promising option for organizations wishing to reduce costs while increasing energy security and islanding time. A prime example of such an organization is the U.S. military, which often operates in isolated areas and whose reliance on a fragile commercial electric grid is seen as a security risk. However, incorporating renewable sources into a microgrid is difficult due to their typically intermittent and unpredictable nature. We use simulation techniques to investigate the performance of a hypothetical hybrid microgrid containing both wind turbines and fossil fuel based power sources. Our simulation model produces realistic weather forecast scenarios, which we use to exercise our optimization model and predict optimal grid performance. We perform a sensitivity analysis and find that for day-ahead planning, longer planning horizons are superior to shorter planning horizons, but this improvement diminishes as the length of the planning horizon increases.

The development of software that controls real life processes can be highly difficult and error prone. In the case that the destination test-bed does not fully exist, the situation becomes significantly more challenging. We developed a control software for charging processes of an electric vehicle fleet in a smart grid architecture. To accelerate the development and to ease the integration process, we used an agent-based approach and embedded the optimization software within a simulation environment. Later we enhanced this simulation environment to a consultant tool which can be used to assess the impact of structural extensions. In this paper we present both, the optimization mechanism as well as the simulation environment.

Growing concerns with environmental issues have led to the consideration of alternatives to urban mobility. Among available options, electric vehicles have been considered in advantage in terms of sustainability as well as emission of pollutants. This work presents an optimized solution to allocate electric charging stations based on a simplified traffic model for urban mobility and vehicles’ energy consumption. It is particularly interesting for prototypes and initial studies on deploying charging stations. A discrete event simulation is built in Arena and an optimization is implemented with OptQuest package. The simulation model considers stochastic information whose characterization is difficult to obtain for particular cases. The results show that there are several variables that can be correctly determined to avoid prohibitive costs in the deployment of charging stations.

Worldwide, and in particular in Germany, a trend toward a more sustainable electric energy supply including energy efficiency and climate protection can be observed. Simulation models can support these energy transitions by providing beneficial insights for the development of different electricity generation mix strategies in future electric energy systems. One important input parameter for these large-scale simulations is the electricity demand, commonly obtained using empirical datasets. However, it is desirable to deploy dynamic electricity demand models to be able to investigate the behavior of the energy system under changing or specific conditions. In this paper we present such a model. We identify the most important parameters, such as the seasonality, the type of day, and the daily mean temperature to accurately model the large-scale electricity demand for Germany. We validate and implement our model in the context of a hybrid simulation framework and show its correctness and applicability.

New challenges demand that manufacturing companies adopt sustainable approaches and succeed in this adoption. Energy efficiency plays a key role in achieving sustainability goals, and performance indicators are necessary beyond measurement of data to evaluate energy efficiency . In this landscape, scalable and easy-to-understand metrics providing an energy competitiveness degree of manufacturing resources are currently missing. The study aims to test through simulation applicability and potential offered by a novel Energy Overall Equipment Effectiveness – Energy OEE - indicator for discrete manufacturing firms. A simulation of a discrete manufacturing CNC machine case is used to evaluate the applicability of using Energy OEE assessment for management decision support. As a result, this study paves the way to a better exploitation of data that energy monitoring and sensor technology aim to offer in the future.

Buildings are one of the major energy consumers because of the need to meet occupants requirements. The commercial/institutional sector accounted for 14% of total energy consumption in Canada in 2009 while office buildings consumed 35% of this amount. Auxiliary equipment used 19% of the total energy consumed in office buildings. Previous studies showed the impact of occupancy behavior on IT equipment energy consumption. This paper proposes a new method for monitoring occupant behavior and energy consumption of IT equipment. Analyzing the resulting data can help evaluating the occupancy behavior impact on energy saving. Two wireless sensor technologies are investigated to collect the required data and to build an occupancy behavior estimation profile: Ultra-Wideband Real-Time Location System for occupancy location monitoring and Zigbee wireless energy meters for monitoring the energy consumption of IT equipment. The occupancy behavior estimation profile can be used to reduce energy consumption based on real-time occupants’ information.

Water is a key issue in sustainable urban development. SWIM (Simulating Water, Individuals and Management) is an agent-based model of water supply, management structure, and residential water consumer perception and behavior. Initial work applied data mining on newspaper articles to map networks of water management institutions and structures. SWIM extends this by linking an agent-based model of residential water consumption connected via networks of water managers to a global-scale hydrological model. In our case study, we focus on Tucson, AZ, where management and social behaviors are well documented. Census data are used to create synthetic populations of consumers endowed with price sensitivity and behaviors impacting water use. Social networks, including those based on geographic proximity, allow water use behaviors to spread to others. We examine possible factors leading to recent attested declines in per-capita water use, leveraging ensemble runs on high-performance computing resources to strategically explore complex parameter spaces.

Purchasing activities consume more than half of manufacturing and trading organizations sales capitals. Effective procurement is tied with efficient and highly accurate collection of data needed for purchasing the right material with the acceptable quality from appropriate suppliers. Supply chain management (SCM) consists of complex networks of distributed actors in which the problem of identifying the appropriate suppliers and allocating optimal order quantities based on the Triple Bottom Line (TBL) attributes is strategically important. However, implementation of an autonomous and automated assessment that can incorporate dynamics and uncertainty of the whole supply chain during the assessment period is not addressed. In the current research paper, a novel framework is designed and proposed to narrow the aforementioned gap. Agent technology has been incorporated in the developed framework to decrease the supplier chain uncertainty by decreasing human interactions and automating the process of supplier evaluation and order allocation.

The block time (BT) schedule, which allocates Operating Rooms (ORs) to surgical specialties, causes inflexibility for scheduling outside the BT, which negatively affects new surgeons, new specialties, and specialties that have fluctuation in the number of surgeries. For this inflexibility, we introduce the concept of releasing ORs, and present a generic simulation and evaluation framework that can be used by hospitals to evaluate various release mechanisms. The simulation and evaluation framework is illustrated by a case study at Vanderbilt Medical Center and University (VUMC) in Nashville. The results show that introducing a release policy has benefits in decreasing the number of unscheduled patients and decreasing access time, without affecting the specialties originally assigned to the released rooms.

Before the day of surgery, it is common for hospitals to take advantage of block release time in order to better fill operating rooms (ORs) and increase room utilization levels. Surgery groups are forced to release unscheduled OR time, which then becomes available for other groups to use. In this paper, we investigate release policies based on various surgery arrival distributions, capacity levels, and case durations. We show the tradeoffs of different policies involving assigned block and open posting rooms’ utilization levels and number of cases not accommodated in the schedule. Our results show that block release has a minor benefit for services with high room utilization (at or above 80%). Services with lower room utilizations may benefit from release, but one must consider whether to use block release or to reallocate the service’s block time.

Perioperative services have a high impact on a hospital’s financial success. In order to increase patients’ privacy and satisfaction, while restraining cost, a redesign of the existing Post-Anesthesia Care Unit (PACU) was suggested at the Mayo Clinic. A simulation model was created to determine the number of beds required in the redesign of the PACU to maintain Operating Room (OR) blocking below 5 %. Since OR time and resources are more costly than the PACU, limiting the resource scarcity of the PACU should minimize delays through the surgical suites. By assuming PACU resourcing as secondary to managing the OR, the underlying complexity of the surgical scheduling did not have to be analyzed. Real data was fed into the simulation model that successfully captured the complexity of the system without the work-intensive requirements of theoretical modeling. The results of the analysis were incorporated into the design plans for remodeling the PACU.

The purpose of this study is to provide a user-friendly Excel simulation tool for primary care practices to manage appointment schedules which accommodate multiple patient types and stochastic service times for the nurse and provider steps in patient flow. The key features of the Excel tool are: a color-coded schedule of each provider; a Gantt chart as a visual aid for schedulers; dynamic tests of different patient mix, sequence and start time combinations; and performance measures that include average and key percentiles of wait time, idle time and session completion time. The Excel tool can be easily modified by practices to incorporate their own data and needs. In our case study, we quantify the impact of flexible nurses and/or providers to satisfy patient demands for multiple providers.

In this paper we develop a framework that integrates a discrete-event simulation model and integer programming (IP) for patient admission planning and the intermediate term allocation of resource capacities. Two types of patients are considered in this study: new and existing. The simulation model is used to find the best balance between new and existing patients arriving to each appointment time period during the day. New patients require more time to complete their admission processes and for visiting with a doctor. The IP produces an optimal calendar schedule for the doctors, i.e., the best appointment time for each doctor to see new patients. We report on computational results based on a real clinic, historical data, and both patient and management performance measures.

Oncology clinics face several complexities in their processes. When patients arrive at the infusion chairs, nurses and pharmacy technicians must be available to get the patients ready for the infusion and mix their drug treatments. This requires having the right information at the right moment. This research develops a detailed discrete event simulation model which considers the interactions between resources, information, and patient flow. The model was used to evaluate different scheduling policies and determine which of them could be incorporated in the clinic with the objective of increasing daily throughput without affecting patient wait time or total time in the system.

We develop an empirically calibrated hospital-wide simulation model to represent a time-varying, multi-server queuing network with multiple patient classes. Our main focus has been on quantifying the impact of discharge profiles to alleviate inpatient bed congestion. A discharge profile is defined by (a) discharge window, which specifies hours of the day discharges are allowed; and (b) the maximum capacity for discharges in each hour of the window. Results of our simulation model show that a more responsive discharge policy that prioritizes discharges in units with longer admission queues can significantly reduce waiting times (40% reduction in queues). In comparison, an early-in-the-day discharge policy has a lower impact on improving bed congestion; we also find that early-in-the-day discharges are very hard to realize in practice. Further, expanding the discharge windows by only 2 hours in the evening (7- 9 PM) creates the same benefit, and is more realistic.

Cardiovascular disease (CVD) is the leading cause of death in the U.S. and has placed heavy economic burden on the healthcare system. Recognizing the importance of CVD prevention, the American Heart Association (AHA) recently identified several most important risk factors of CVD and proposed a concept of ideal cardiovascular health. Based on this concept, we developed an agent-based model which is designed to capture the individual health progression and study the emergent CVD-related population health outcomes (i.e., diabetes, myocardial infarction, stroke, and death). The numerical results demonstrate the predictive validity of the model and also show how the model could be used in practice by assessing the impact of a set of hypothetical lifestyle interventions on CVD-related health outcomes. With the strength of capturing population heterogeneity and health progression stochasticity, our model is expected to help policy makers design more effective intervention programs for the population of their interest.

This paper presents a discrete event simulation-based analysis of the Radiation Therapy (RT) care delivery process at the Radiation Oncology Center of the University of North Carolina (UNC) at Chapel Hill aimed at improving process reliability and patient safety. The use of quality assurance (QA)checklists and people-based safety barriers (SBs) in radiation oncology are widely recognized methods for detecting potential human errors before they reach the patient. In this study, data on patient safety events (an incident that reached the patient, whether or not the patient was harmed) and near misses (an incident that comes close to reaching the patient but is caught and corrected beforehand) were collected through a comprehensive safety program and used to estimate event rates and reliability score for each QA and SB. Recommendations for reducing risk of events by using the most effective combination of QA/SBs is provided.

In 2010, over 200,000 women in the United States were diagnosed with invasive breast cancer, and an estimated 17% of those women died from the disease, according to the Centers for Disease Control and Prevention (CDC). Also in 2010, the CDC reported that 12.6 million women have diabetes, the seventh leading cause of death in the U.S. Although we know much about these prevalent diseases individually, little research has been conducted regarding the interaction between breast cancer and diabetes. In this study, we build a simulation model framework that explores this complex relationship, with an initial goal of assessing the prognosis for women who are diagnosed with diabetes considering their breast cancer risk. Using data from national survey and surveillance studies, we characterize morbidity and mortality. This framework would potentially allow us to study a variety of diseases that are comorbid to breast cancer.

We design and develop a predictive model that estimates health and economic outcomes associated with smoking cessation interventions using discrete-event simulation (DES). Outcomes include estimates of sustained abstinence from smoking, quality of life years gained, cost of treatment, additional health-related morbidity due to long-term effects of smoking (e.g. lung cancer, stroke), and cost-effectiveness of the various smoking cessation options. Interventions assessed include nicotine replacement therapy (patch or gum), oral medication (bupropion and varenicline), and abstinence without pharmacologic assistance. The DES approach allows us to account for heterogeneity of patients and dynamic changes in disease progression. Results show that even a single quit attempt can be cost-effective over the patients’ lifetime. Furthermore, based on the incremental cost-effectiveness ratios, varenicline dominates other treatments at 10 years, 30 years, and over the lifetime. Understanding the comparative effectiveness and cost of alternative smoking cessation strategies can improve clinical and patient decision-making.

Cesarean delivery is the most common major abdominal surgery in many parts of the world. As of October 2012, the cesarean section rate in the United States was reported to be 32.8% in 2011, rising from 4.5% in 1970. Cesarean sections are associated with an increased risk of neonatal respiratory morbidity, increased risk of a hysterectomy and can cause major complications in subsequent pregnancies, such as uterine rupture. To evaluate the current cesarean delivery rate due to a “failure to progress” diagnosis, our goal was to replicate the delivery process for women undergoing a trial of labor. In this simulation we evaluate the Friedman Curve and other labor progression rules to identify circumstances in which the cesarean rate can be decreased through the analysis of the total length of time a woman spends in labor as well as the duration of time a woman remains in a cervical dilation stage.

Eighty percent of Brazilian population is assisted by the public health system (SUS), while the rest uses private health insurance. The existing number of beds do not meet the entire demand and Brazilian government has made efforts to improve the system. The Health Ministry has analyzed a new proposal for planning the bed capacity. This proposal uses a queuing model and a new categorization of hospitalization and beds, taking into account specialties, age of the patients, and appropriate occupancy rates. In this paper, we use the proposed technique to estimate the required beds for Belo Horizonte, a mid-sized city in Brazil. We compare number of beds required by centralized and non-centralized administration. A simulation model was developed to analyze the dynamic behavior of the system and searching for the best configuration. This model was used to evaluate the results obtained by queuing model and to check its usability.

In this paper we use simulation to evaluate the effect of shorter red cell shelf life on blood supplies at the Mayo Clinic and compare these results to previous work. Results show that a reduced Maximum Shelf Life of 28 days is supportable under current conditions but that a maximum shelf life of 21 days or less will likely result in unacceptably high outdating rates or unmet patient demand. We also compare the result of discrete event simulation to those of a simple excel-based simulation and find that the excel-based simulation predicts a smaller increase in outdating rate in the same scenarios.

This paper presents a sensitivity analysis of unit capacity and patient flow for a hospital-wide system consisting of interdependent clinical and ancillary departments. The research employs system dynamics to model a hospital-wide system representative of a medium size, semi-urban, acute care community hospital. A sensitivity analysis using regression methods examines emergency department performance in the context of the hospital-wide system using a modified formulation of the Overall Equipment Effectiveness (OEE) hierarchy of metrics as a key performance indicator. The modified OEE metric demonstrates its usefulness first for the purpose of conducting a group screening design, and second for the purpose of performing the sensitivity analysis. The main results of the sensitivity analysis indicate that emergency department performance depends significantly on the unit capacity and patient flow in departments hospital-wide. The analysis provides quantitative insight into the important factors, the interactive relationships across departments, and evaluates the overall factor relative importance.

Information Technology in healthcare is an ever-growing enterprise, with medical providers becoming more and more reliant on data to make care decisions. With the increased reliance on these applications for care, questions arise around the availability and manageability of those systems. This paper examines a model which has been developed for the selection of computing infrastructure architectures in healthcare organizations. This model utilizes the Analytics Hierarchy Process (AHP) to weigh the various criteria that come into play for decisions of this nature. Further, to vet the recommendations of the AHP model, and to lend quantitative data to the decision making process, simulations of the various architectural options were built for various application scenarios. The results of these simulations thus serve as additional validation of the model’s efficacy. This paper focuses on the use of discrete event simulation using ExtendSim® to assist in the architectural selection process for computing architectures.

Results of clinical laboratory tests inform every stage of the medical decision-making process, and measurement of enzymes such as alanine aminotransferase provide vital information regarding the function of organ systems such as the liver and gastrointestinal tract. Estimates of measurement uncertainty quantify the quality of the measurement process, and therefore, methods to improve the quality of the measurement process require minimizing assay uncertainty. To accomplish this, we develop a physics-based mathematical model of the alanine aminotransferase assay, with uncertainty introduced into its parameters that represent variation in the measurement process, and then use the Monte Carlo method to quantify the uncertainty associated with the model of the measurement process. Furthermore, the simulation model is used to estimate the contribution of individual sources of uncertainty as well as that of uncertainty in the calibration process to the net measurement uncertainty.

Clinical laboratories play a critical role in patient diagnosis, treatment planning and prevention of disease. The inherent complexity of clinical laboratories lies in the volume and variety of specimen types, which varies by time of day/week and hospital census (e.g., blood draws for rounds); different handling and processing requirements based on patient characteristics (e.g., infants vs adults); the diversity of lab equipment and specialized instruments to perform the tests; and the requirements for appropriately credentialed staff on a 24/7 schedule. Although clinical laboratories reflect many aspects of traditional production systems, the medical profession is—as are most specialized areas of practice—much more willing to entertain modeling approaches that describe their systems with domain-appropriate terminology and semantics. In this paper we discuss the development of a framework for creating domain-specific simulation objects for modeling clinical laboratories; we demonstrate their applicability in a project undertaken with the Chemistry Laboratory at Seattle Children’s Hospital.

Variability in the duration of surgical procedures is one cause of delayed start times for scheduled procedures in operating theaters. While historical procedure durations are frequently used in assigning surgery times to schedule surgery blocks, taking into account the level of variability associated with specific procedures is not commonly utilized in creating surgery schedules in a multiple room operating suite. This article proposes a new methodology for surgical scheduling which sequences procedures based on duration groups and their level of variability. Discrete event simulation was used to model and validate the ratio of delayed starts versus on-time starts due to incorrectly estimated procedure length using a hospital’s current scheduling algorithm and historical data. A statistical analysis was used to compare the proposed methodology against the current scenario to determine if delayed starts can be reduced by sequencing procedures based on duration variability.

Operating room (OR) rescheduling is the process of adjusting the surgery schedule when the current schedule is subjected to disruptions on the day of surgery. The decision to make a schedule adjustment will impact patient safety, patient satisfaction, hospital costs, as well as surgeon satisfaction. Of particular importance is when, and how frequently, to update the scheduling and tracking systems. These questions and their impact on maintaining schedule accuracy and minimizing room overtime are explored. Discrete event simulation was used to simulate surgical cases in the OR and to test different “right shifting” and case updating policies for their effectiveness. Results and staff experience indicate that ten minutes is the preferred delay in which an update should be made; otherwise staff satisfaction or schedule accuracy will suffer.

There are two main types of surgeries within an operating room (OR) suite, namely elective and non-elective. Non-elective surgeries count for a considerable proportion of surgery demand. Accommodating these surgeries can be a challenging task on the day of surgery. This is mainly a result of the uncertain demand for non-elective surgeries, which discourages hospitals from reserving sufficient capacity for them. Using simulation, we evaluate an optimal policy for accommodating elective and non-elective surgeries that minimizes waiting time of patients, overtime, and number of patients turned away. We carry out the analysis on a stylized, two-room study where one is dedicated to non-electives and the other one contains elective cases but can accept non-electives if necessary. The optimal policy is originally found by using a Markov decision process (MDP). However, since MDP has limited assumptions, the evaluation through simulation allows these assumptions to be relaxed.

The research direction in modeling complex, chronic conditions for health policy evaluation has been to incorporate individual heterogeneity. This detail makes our models more powerful and relevant. In implementing an individual simulation model of colorectal cancer we have recognized two considerations related to incorporating individual heterogeneity that have not been adequately discussed in the literature. First, there are substantial computational gains and interpretation benefits if an individual’s life courses are identical except when differences are directly induced by an intervention. Achieving this is not trivial. We have developed the notion of a “common patient” who is the same between scenarios except for intervention-induced changes. We create the common patient using a careful application of Common Random Numbers (CRN). Second, when we model differentiated individuals we can examine differing impacts of polices on specific sub-groups. This leverages the detail in individual attributes to produce useful results for policy makers.

Type2 Diabetes Mellitus (T2DM) and its complications account for 11% of the global health expenditure (IDF 2012). Different primary, secondary, and tertiary preventive interventions promise better health outcomes and cost savings but are often studied separately. This paper proposes a simulation model for T2DM that comprehends the nonlinear interactions of multiple interventions for various stages of T2DM on population dynamics, health outcomes, and costs. We summarize the model, then demonstrate how we addressed the important challenge of fitting input parameters given that data needed to be combined from disparate sources of data sources in a way that calibrates input parameters to output metrics over a range of decision variables (a form of model calibration to achieve a response model match to clinical data). We present preliminary numerical results to inform policies for T2DM prevention and management.

Influenza is a serious public health concern and vaccination is the first line of defense. In a pandemic, individuals are prioritized based on their risk profiles and transmission rates to ensure effective use of available vaccine. We use an agent-based stochastic simulation model, and optimize the age-specific vaccine distribution strategy. We use numerical optimization techniques to minimize the overall cost of the outbreak. Our computational experiments show that the best policy returned by our approach outperforms alternative policies recommended by Centers for Disease Control and Prevention.

We propose a Discrete Event Simulation (DES) model for an emergency department (ED). The model is developed in close collaboration with the French hospital Saint Camille, and is validated using real data. The objective of this model is to help ED managers better understand the behavior of the system and to improve the ED operations performance. The most essential features of an ED are considered in the model. A case study is conducted in order to allow decision makers select the most relevant investment in the human staffing budget. A simulation-based optimization algorithm is adopted to minimize the average Length of Stay (LOS) under a budget constraint. We conduct a sensitivity analysis on the optimal average LOS as a function of the staffing budget, and derive useful recommendations to managers on how the budget can impact the performance of the system.

This paper addresses the hospitalization admission control policies of patients from an emergency department that should be admitted shortly or transferred. When an emergency patient arrives, depending on his/her health condition, a physician may decide to hospitalize him/her in a specific department. Patient admission depends on the availability of beds, the length of stay and the reward of hospitalization which are both patient-type specific. The problem consists in determining patient admission policies in order to maximize the overall gain. We first propose a Markov Decision Process Model for determination of the optimal patient admission policy under some restrictive assumptions such as exponentially distributed length of stay. A simulation model is then built to assess MDP admission policies under realistic conditions. We show that MDP policies significantly improve the overall gain for different types of facilities.

Emergency Rooms (ER) are resource constrained systems that demand efficient management in order to fulfill their care and service objectives. This article explores the use of real-time simulation to improve daily operations at an ER where unplanned events may occur. Such a simulation model must adequately portray the current state of the system; however ER workflow management systems often provide only limited information for this purpose. This paper studies the impact of the model’s ability to predict ER performance with a limited amount of input information, such that what-if questions may be asked to guide decision making. Towards this end, two input data scenarios are compared to a case of perfect information. One scenario considers only patient arrival times and the other assumes additional knowledge of patient care pathways. Although both generate similar performance measures, the case with least information yields a slightly worse estimate of patient care pathway composition.

Multi-objective simulation optimization was performed to synergistically investigate the cost and benefits of the most commonly-used strategies for H1N1 epidemic mitigation: vaccination, antiviral treatment, and school closure. By simultaneously considering the three intervention strategies, this study leads to findings that supplement those in the existing work, and provides additional insights regarding intervention decision making. Specifically, our investigation suggests that different vaccine prioritization strategies, the age-based vs. ACIP (Advisory Committee on Immunization Practices) recommendation, be implemented depending on vaccine availability; individual school closure policies are favored over their global counterparts, at least when both vaccination and antiviral treatment are implemented with relatively plentiful medicine supply. The trade-offs of cost and benefits of the intervention strategies were investigated, and can be used to support relevant decision making.

Tuberculosis (TB) is an infectious disease that can progress rapidly after infection or enter a period of latency that can last many years before reactivation. Accurate estimation of the proportion of TB disease representing recent versus remote (long ago) transmission is critical to disease-control policymaking (e.g., high rates of recent transmission demand more aggressive diagnostics). Existing approaches to this problem through cluster analysis of TB strains in population-based studies of TB molecular epidemiology are crude and prone to bias. We propose an agent-based simulation of TB transmission in conjunction with molecular epidemiologic techniques that enables study of clustering dynamics in relation to disease incidence, diversity of circulating strains, sampling coverage, and study duration. We perform a sequence of simulation experiments with regard to different levels of each factor, and study the accuracy of estimates from the cluster-analysis method relative to the true proportion of incidence due to recent transmission.

Dengue fever represents a great challenge for many countries, and methodologies to prevent and/or control its transmission have been largely discussed by the research community. Modeling is a powerful tool to understand epidemic dynamics and to evaluate costs, benefits and effectiveness of control strategies. In order to assist decision-makers and researchers in the evaluation of different methodologies, we developed DengueME, a collaborative open source platform to simulate dengue disease and its vector’s dynamics. DengueME provides a series of compartmental and individual-based models, implemented over a GIS database, that represents the Aedes aegypti’s life cycle, human demography, human mobility, urban landscape and dengue transmission. The platform is designed to allow easy simulation of intervention scenarios. A GUI was developed to facilitate model configuration and data input.

A hierarchical modeling and simulation (M&S) framework can help federal agencies integrate the myriad business resourcing decisions they face as unmanned aerospace vehicle (UAV) systems are deployed within their federally authorized charters. An integrated M&S method offers a pragmatic approach to leveraging the power of analytical techniques and coping with the complex support requirements of modern macrosystems. In this paper, we demonstrate the benefits of incorporating several agent-based modeling (ABM) enhancements for UAV route planning into a hierarchical M&S structure.

Forest fires are time evolving disasters that consume environmental and financial resources, endangering the rescue units that try to mitigate them. As such, a simulator that can predict the fire propagation is essential to locate control systems, in order to reduce the loss of natural resources without risking the firefighters. This paper proposes a simulator based on discrete representation of the selected areas where the velocity of fire propagation between neighbors depends on variables associated with locative or climatological characteristics. We consider discrete classification based on the spread in each direction. To validate our simulator we considered two scenarios: a theoretical area to test the algorithm considering complete information characteristics and a forest area near Bogota, Colombia. The results show realistic propagation patterns compared to region real past forest fire events. For a better prediction we need more reliable data and relate the fire to both location and weather characteristics.

The need for appropriate evacuation strategies is always a daunting problem when confronted with a large-scale natural disaster. The challenge is to find a policy that maximizes the number of survivors and minimizes the total cost simultaneously under a set of resource and geographic constraints. We develop an agent-based discrete event simulation (ABDES) evacuation framework based on an embedded geographic information system (GIS) module to solve a network evacuation problem that involves multiple candidate shelters, multi-priorities evacuees and several vehicle types. The evacuation framework consists of three interacting components: a disaster scenario generator module, a GIS module for analyzing an evacuation network, and an ABDES module. We conduct experiments using the city of Galveston as an example. The evacuation framework offers insight to decision-makers about the number and location of shelters, allocation and assignment of evacuation vehicles, and distribution of relief resources that are required to complete a large-scale evacuation.

The development of predictive tools for emergency management has recently become a subject of major consideration among emergency responders, especially at the federal level. Often the news of an impending high-consequence threat causes significant stress on these agencies because of their inability to apprise management of probable impacts with sufficient certainty. This paper documents Argonne National Laboratory’s effort to demonstrate the predictive capability of its newly enhanced tool called EPfast in estimating the impacts of postulated events on our power system. Specifically, the study focuses on EPfast’s ability to estimate power outage areas resulting from random system contingencies. The San Diego September 8, 2011, blackout that affected most of southern California was selected for simulation using EPfast. Results showed agreement with actual reported impacts in both spatial and quantitative terms. The method, assumptions, and data used are presented here, and results showing their potential application to emergency planning are discussed.

Agent-based modeling (ABM) has gained great popularity in recent years, especially in application areas where human behavior is important, because it opens up the possibility of capturing such behavior in great detail. Hybrid models which combine ABM with discrete-event simulation (DES) are particularly appealing in service industry applications. However in this paper we argue that many of the so-called distinctions between agents in an ABM and entities in a DES are artificial, and we describe several DES models which use standard entities to represent agent-like behaviors.

Simulation models have been used extensively to evaluate and aid in planning of health screening strategies in health care. In typical simulation models, patient screening behavior is often assumed as homogeneous (patient characteristics do not influence the probability of undergoing screening) and rigid (invariant with time, and not malleable by screening interventions), and modeled as such. However, patient screening behavior in reality is heterogeneous and malleable. Disregarding these realities affect the model functionality and modeling outcomes. We propose two general simulation model structures: one representing typical existing simulation model and another representing simulation model that incorporates heterogeneity and malleability of patient screening behavior. We then illustrate both model structures by implementing it in the case of Diabetic Retinopathy eye screening. Comparison of the two resulting models indicate that heterogeneity and malleability of patient screening behavior should be addressed in simulation models to improve model functionality and precision of outcome estimates.

This paper discusses the development of a simulation model to mimic a return to work phenomenon of Social Security Disability Insurance (SSDI) enrollees in the United States. Agent Based and Bayesian Network methods are used within a multi-method simulation model to capture system conditions and enrollee behavior. Bayesian Network is used within an agent to represent enrollee’s decision to work. A developed simulation model can be used to investigate many aspects of the return to work phenomenon. The model is used to answer a sample research query that examines how the perception of an enrollee on work incentives related to health improvements, money, and vocational assistance can affect the return to work phenomenon for 18 to 39 year old SSDI enrollees (at enrollment). This is measured as the total percentage of population with benefits terminated for work.

Modern healthcare reforms are required to be financially, environmentally and socially sustainable in order to address the additional constraints of financial resources shrinkage, pressure to reduce the environmental impacts and demand for improving the quality of healthcare services. Decision makers face the challenge of balancing all three aspects when planning. However, implementing such an approach, particularly in healthcare, is not a trivial task. Modeling & simulation is a valuable tool for studying complex systems. This paper investigates the application of a hybrid approach that combines Agent-based Modeling & Simulation (ABMS) and Discrete-Event Simulation (DES) for analyzing sustainable planning strategies for Emergency Medical Services. The paper presents a case study that shows how combined ABMS and DES models can support strategic planning and simulation analytics, respectively. The generated data from the ABMS is fed to the DES model in order to analyze the different strategies and the preliminary results are promising.

Hybrid simulation, the combination of simulation paradigms to address a problem is becoming more popular as the problems we are presented with become more complex. This is evidenced by an increase in the number of hybrid papers published in specific domains and the number of hybrid simulation frameworks being produced across domains. This paper focuses on two hybrid simulation models from a healthcare context. The first uses system dynamics and discrete-event simulation and was developed using two separate software tools (Vensim and Simul8). The second uses agent-based and discrete-event simulation and was developed in a single software environment, Anylogic. The reflections on these models add to the debate about the viability of hybrid modelling and suggest future steps to support the take up of the approach.

A hybrid simulation model is a simulation model that is formed from at least two different simulation modelling methods (e.g., discrete event, system dynamics, agent-based). The use of different simulation modelling methods in one model requires modellers to specify additional model elements. This paper discusses three elements, namely, the modules, module interfaces and updating rules. Each module may use a different simulation method. The interface between modules defines the information that will be passed between them (including aggregation and disaggregation). The updating rules define how the information sent by one module affects other modules. These three elements are explained using a case study of a blood-supply chain simulation model for low- and middle-income countries (LMIC) which has different characteristics and challenges in comparison to the typical blood supply chain in high-income countries (HIC).

Boeing Commercial Airplanes produces four twin-aisle airplane models at its Everett, Washington production facility—the largest building by volume in the world. Efficient and effective material handling of large airplane substructures is critical to maintain production rates, and the Everett facility employs two interconnected systems of overhead cranes to move airplane sections through the factory. The crane scheduling team needed a tool to evaluate current and proposed crane schedules for feasibility, rate capability, and potential bottlenecks. Boeing Research and Technology partnered with Simio LLC to develop a simulation model of the crane network that would execute and evaluate a series of crane moves. The model employs both discrete event and agent-based paradigms to model the complex system and to allow for highly configurable initial states. This approach allows for rapid schedule evaluation, non-recurring planning, and real-time system modeling. In this paper we present the system, the model, and results.

In this paper, we present an iterative scheme integrating simulation with an optimization model, for solving complex problems, viz., job shop scheduling. The classical job shop scheduling problem which is NP-Hard, has often been modelled as Mixed-Integer Programming (MIP) model and solved using exact algorithms (for example, branch-and-bound and branch-and-cut) or using meta-heuristics (for example, Genetic Algorithm, Particle Swarm Optimization and Simulated Annealing). In the proposed Iterative Simulation-Optimization (ISO) approach, we use a modified formulation of the scheduling problem where the operational aspects of the job shop are captured only in the simulation model. Two new decision variables, controller delays and queue priorities are used to introduce feedback constraints, that help exchange information between the two models. The proposed method is tested using benchmark instances from the OR library. The results indicate that the method gives near optimal schedules in a reasonable computational time.

Workers cross-trained with multiple tasks can improve the workforce flexibility for the plant to handle variations in workload. Therefore, it is necessary to study the dynamic multi-skilled workforce planning problem of production line with the application of cross-training method. The conclusion is helpful to economize the cost of human resources when workload is low and enhance the productivity in the opposite case. This paper studies the dynamic multi-skilled workforce planning problem by exploring the effect of worker pool size and cross-training level on the performance of production line through simulation. A hybrid simulation model is built as the platform to study this problem with Discrete-event Simulation (DES) method and Agent-based Simulation (ABS) method. Besides, the effect of worker learning and forgetting is inevitably considered accompanied with the introduction of cross-training method and its impact on production line will be illustrated.

The computational study of complex systems increasingly requires model integration. The drivers include a growing interest in leveraging accepted legacy models, an intensifying pressure to reduce development costs by reusing models, and expanding user requirements that are best met by combining different modeling methods. There have been many published successes including supporting theory, conceptual frameworks, software tools, and case studies. Nonetheless, on an empirical basis, the published work suggests that correctly specifying model integration strategies remains challenging. This naturally raises a question that has not yet been answered in the literature, namely 'what is the computational difficulty of model integration?' This paper’s contribution is to address this question with a time and space complexity analysis that concludes that deep model integration with proven correctness is both NP-complete and PSPACE-complete and that reducing this complexity requires sacrificing correctness proofs in favor of guidance from both subject matter experts and modeling specialists.

This paper is the first from a series of papers that aim to develop a theory of multi-method M&S approach. The aim of this paper is to develop ontological basis for multi-method M&S approach. The first part of this paper discusses terms related to the use of more than a single modeling & simulation (M&S) method. This is to show the ontological ambiguity currently present within the M&S field in the context of using more than a single method. Next section provides the philosophical stance of the authors about the main terms in order to provide clarification and context of the term multi-method M&S approach. The last section takes these previous concepts and proposes a set of definitions relevant to a multi-method M&S approach, including its parent and derivative terms.

A simulation study consists of several well-defined stages, e.g., problem formulation, model implementation and experimentation. The application of multiple techniques in the model implementation stage is referred to as hybrid simulation, which we distinguish in this paper from a hybrid M&S study, the latter referring to studies that apply methods and techniques from disciplines like Operations Research, Systems Engineering and Computer Science to one or more stages of a simulation study. We focus on the first stage of a simulation study (and by extension a hybrid M&S study), viz., eliciting the system requirements, and conduct a review of literature in Soft Systems Methodology for healthcare operations management. We discuss the potential for the use of Qualitative System Dynamics as an additional soft OR method, complementing (rather than supplanting) existing approaches, which can further aid the understanding of the system in the problem formulation/conceptual modelling stage of a Hybrid M&S study.

Like most countries, Canada faces rising rates of diabetes and diabetic ESRD, which adversely affect cost, morbidity/mortality and quality of life. These trends raise great challenges for financial, human resource and facility planning and place a premium on understanding tradeoffs between different intervention strategies. We describe here our hybrid simulation model built to inform such efforts. To secure computational economies while supporting upstream intervention investigation, we use System Dynamics to characterize evolution of the health, body weight and (pre-diabetes) diagnosis status of non-diabetics. Upon developing diabetes, population members are individuated into agents, thereby supporting key functionality, including accumulation of longitudinal statistics, and investigation of differential treatment regimes based on patient history. Finally, discrete event modeling is used to characterize patient progression through health care processes, so as to capture impact of resource availability, enforce queuing discipline, etc. The paper discusses model findings and tradeoffs associated with the architecture.

This paper proposes a multi-paradigm modeling framework (MPMF) for modeling and simulating problem situations (problems whose specification is not agreed upon). The MPMF allows for a different set of questions to be addressed from a problem situation than is possible through the use of a single modeling paradigm. The framework identifies different levels of granularity (macro, meso, and micro) from what is known and assumed about the problem situation. These levels of granularity are independently mapped to different modeling paradigms. These modeling paradigms are then combined to provide a comprehensive model and corresponding simulation of the problem situation. Finally, the MPMF is implemented to model and simulate the problem situation of representing the spread of obesity.

Modelling real workforce choices accurately via Agent Based Models and System Dynamics requires input data on the actual preferences of individual agents. Often lack of data means analysts can have an understanding of how agents move through the system, but not why, and when. Hybrid models incorporating discrete choice experiments (DCE) solve this. Unlike simplistic neoclassical economic models, DCEs build on 50 years of well-tested consumer theory that decomposes the utility (benefit) derived from the agent’s preferred choice into that associated with its constituent parts, but also allows agents different degrees of certainty in their discrete choices (heteroscedasticity on the latent scale). We use DCE data in populating a System Dynamics/Agent Based Model – one of choices of optometrists and their employers. It shows that low overall predictive power conceals heterogeneity in agents’ preferences which when incorporated in our hybrid approach improves the model’s explanatory power and accuracy.

The offshore wind industry in Europe are looking to move further from shore and increase the size of wind parks and wind turbines. As of now marine logistics during the operation and maintenance life cycle phase is, besides wind turbine reliability, the most important limitation for wind turbine service, repair and replacement, and pose a large risk for wind park operators and owners. This paper presents a marine logistic simulation model for the O&M life cycle phase based on a combination of the agent-based and discrete event modeling paradigms, currently being tested as a decision support tool by European offshore wind park developers. The model simulates the O&M lifecycle phase of an offshore wind park with all integral components of marine logistic needed. In this paper the simulation model is described together with an application example demonstrating how the simulation model can be applied as a decision support tool.

Many hardly understandable and complex real-world problems can be solved by discrete or continuous simulation techniques, such as Discrete-Event-Simulation, Agent-Based-Simulation or System Dynamics. In recently published literature, various multilevel and large-scaled hybrid simulation examples have been presented that combine different approaches in common environments. Many studies using this technique in interdisciplinary projects have the problem of a different model understanding. In this case, paradigm hiding can help in domain-oriented communication with non-technical experts avoiding unnecessary paradigm discussions. Another problem is that already solved problems are often not reusable in future scenarios and have to be modeled and validated in similar studies from the scratch. This paper presents selected concepts that can help to build domain specific frameworks with reusable components in AnyLogic 7 and depicts two examples; HealthDS for prospective healthcare decision support and i7-AnyEnergy that can be used for building innovative energy scenarios.

A three-level, hierarchical system for yard crane (YC) management in container terminals and the algorithms for the bottom two levels were proposed in previous studies. The bottom two levels are responsible to YC job sequencing and intra-row YC deployment. This paper presents YC deployment strategies for inter-row YC deployment. The objectives are to minimize vehicle waiting times and the number of overflow jobs. We show by realistic simulation experiments that (1) when the number of yard cranes is less than the number of yard blocks, deploying YCs in proportion to the number of jobs in each row (3L-Pro-Jobs) is the best; (2) when the number of yard cranes is equal to or more than the number of yard blocks, the apparent workload approach, 3L-AW, performs best.

Yard crane (YC) dispatching in the operational planning of container terminals usually aims to minimize makespan of YC operations or waiting time of vehicles. We propose that minimizing the maximum tardiness of vehicle jobs at yard blocks will minimize the operational delay of the longest quay crane (QC). This will minimize vessel turnaround time which is one of the most important objectives of container terminals. A provably optimal algorithm, MMT-RBA* to minimize maximum job tardiness, is presented to sequence the YC jobs. Jobs requiring reshuffling of other containers, often ignored in other studies, are handled by embedded simulation in our optimization algorithms. Another provably optimal algorithm, MMS-RBA* to minimize makespan, is also presented. Simulation experiments confirm that MMT-RBA* significantly outperforms the optimal algorithm RBA* to minimize vehicle waiting time and MMS-RBA* to minimize makespan in minimizing vessel turnaround time.

To provide better services for shipping companies and to increase profits, improving the operation equipment efficiency and reducing the ship dwelling time at the terminal is an important problem for port companies. Port management information system and information technology is widely used for supporting and controlling the terminal operation, which can track the operation data simultaneously. In this study, a simulation model is constructed using historical Global Positioning System tracking data and analyzed for application to the shipping industry. For improving handling equipment efficiency, flexibility analysis is performed for the trailers that served gantry cranes for ship operation (container loading and unloading processes) by comparing the different dispatching scenarios after performing the simulation. The proposed procedure to construct simulation models of container terminal was found to be both practical and powerful.

In this paper various Inter Terminal Transport (ITT) systems for the Port of Rotterdam are evaluated. The Port Authority is investigating possible solutions for the transport of containers between terminals at the existing Maasvlakte 1 and the new Maasvlakte 2 areas. A discrete event simulation model is presented that incorporates traffic modeling, which means that delays occurring due to traffic will have an impact on the system's performance. The model is applied to 4 different ITT vehicle configurations, including Automated Guided Vehicles (AGVs), Automated Lifting Vehicles (ALVs), Multi Trailer Systems (MTSs) and a combination of barges and trucks. Furthermore, 3 realistic demand scenarios for the year 2030 are used for the analysis.

Terminal operating systems (TOS) play a major role in today’s terminal performance. A TOS is used to create operational plans in order to ensure timely handling of vessels, trucks and trains at minimal operational cost. Creating appropriate operational plans forms a challenge for the planners, as plans should take into account a variety of aspects, such as grounding decisions and equipment dispatching. In addition, several decisions have to be made within a limited time frame, couple of hours before starting the operation. Wrong decisions in the plan can have major impact on the operation and implies financial and safety consequences. Planners face these problems only when the plans are actually executed in live operation. In this article, we propose to use a fast yet accurate simulation of both the operation and the TOS in order to support planners to investigate and adapt plans before they are applied in live operations.

Various actors are involved in hinterland transportation of incoming rail containers along the maritime transport chain. To coordinate each actors logistics processes, and therefore to improve utilization of existing transport capacity, the early provision of information, e.g. in form of estimated time of arrival (ETA), is inevitable. The objective of this paper is to measure impacts of these information flows on capacity utilization via a simulation based approach. To simulate the effect of ETA container from vessel to hinterland transport mode rail, a system dynamic simulation model is developed based on a case study about input containers at the port of Hamburg. As result the container output on rail is compared with and without ETA for different container input volumes. It will be shown, managing provision of information in supply chains – such as maritime transport chains – is a valuable approach for increasing existing utilization.

Central European inland waterways are presently utilized way below their theoretical carrying capacity. For instance, cargo transported on the Danube is only 10-20% of that transported on the Rhine. To support an increase of transport flows on inland waterways (especially container transport on the Danube) and to contribute to a significant modal shift from road to waterways, operators have to be enabled to improve their economic position by improving the material flow in the handling points of the intermodal transport chain, container terminals, which oftentimes form a considerable bottleneck due to e.g., long processing times. In this paper, gaps in the material flow of container terminals alongside the Danube are revealed with the use of simulation. The Simulation enables the terminal operator to create an experimental model and decide on the best recommended course of action.

As the bottleneck of the shipping routes from the Indian Ocean to the Pacific Ocean, the Singapore Strait is handling high daily traffic volume. In order to enhance navigational safety and reduce collisions at sea, several approaches have been proposed. However, most of the contributions adopt deterministic algorithms, failing to consider the stochasticity due to human behaviors of ship captains. Moreover, the effectiveness of these approaches is hindered by the fact that their focus is on providing a globally optimal safe set of trajectories to all vessels involved in encounter situations, almost neglecting each captain’s perspective. We propose Safe Navigation Assistance Tool (SNAT), a simulation–based search algorithm to assist the captain by suggesting highly safe and robust maneuver strategies for conflict avoidance. Extensive numerical experimentation were performed proving the effectiveness of SNAT in reducing the number of conflicts, with respect to real data provided by the Automatic Identification System (AIS).

As the global container logistics, especially the transshipment services, keep increasing, substantial improvements on both port storage capacity and port throughput rate are necessary. Besides, the future challenges of getting skilled labor and the rising labor cost have been bothering port operators. Automated Container Terminals (ACT) is a promising solution to these challenges. This study firstly introduced two new conceptual transshipment hub designs, i.e. GRID-ACT and FB-ACT. Then the simulation models were designed respectively and the analytical results revealed both pros and cons for both systems. Besides, the land utilization and capacity of two ACTs have also compared among advanced contemporary ports in the world.

Projects in mechanized tunneling frequently do not reach their targeted performance. Reasons are often related to an undersized or disturbed supply-chain management of the surface jobsite. Due to the sensitive interaction of production and logistic processes, planning and analyzing the supply-chain is a challenging task. Transparent evaluation of chosen logistic strategies or project setups can be achieved by application of process simulation. This paper presents the continued work of a simulation approach to analyze the complex system of mechanized tunneling with special focus on the internal logistic as a part of the jobsite supply-chain. The generic implementation allows a flexible configuration of jobsite elements to compare possible setups. A case study demonstrates the approach and highlights the sensitive interaction of production and logistic processes under the influence of disturbances. Additionally, improvements to the original setup of the case study’s construction equipment can be derived.

This paper describes a logistic study about an underground gold mine, belonging to AngloGold Ashanti, where four different layout options could be applied to the tunnels, and also different transportation strategies. Each evaluated layout had its own configuration for shaft and truck fleet. The study was made individually for each year of the mine operation life, determining the necessary transportation capacity to achieve the planned production at that year. Due to the very restrictive traffic options in the tunnels, a framework was developed to represent the tunnels and traffic rules in a discrete-event simulation model. The results pointed the scenario with the lowest necessary transportation capacity to achieve the planned production.

Experiments are key to characterize, model and optimize engineering systems. The use of computer models and hence computer simulations, have allowed engineers to predict the effect of dozen and sometimes hundreds of variables at a specific time in a particular system. The combinatorial explosion that results from using classical techniques to generate experimental designs, however, has hampered such capability. Many analysis tasks, such as simulation optimization and simheuristics, will be importantly enhanced with the possibility of dealing with dozens of variables at a time in a convenient manner. In previous work we identified a series strategies to this end. The objective of the present study is to propose a costing approach to compare these strategies. In addition, designs for 10, 20 or 50 variables and their assessment are made readily available online to different users interested in simulation-optimization based on experimental design, as illustrated here with 50 variables.

This paper reviews the main concepts and existing literature related to the use of biased randomization of classical heuristics and the combination of simulation with metaheuristics (simheuristics) in order to solve complex combinatorial optimization problems, both of deterministic and stochastic nature, in the popular field of Vehicle and Arc Routing Problems. The paper performs a holistic approach to these concepts, synthesizes several cases of successful application from the existing literature, and proposes a general simulation-based framework for solving richer variants of Vehicle and Arc Routing Problems. Also examples of algorithms based on this framework successfully applied to concrete cases of Vehicle and Arc Routing Problems are presented

This paper proposes a new method for creating future itineraries based on a hybrid solution of simulation and optimization techniques. The Mixed-Integer Programming (MIP) technique is used to solve an itinerary generation problem with the objective to maximize the aircraft utilization of the itinerary structure of the flights. The simulation technique is then used to evaluate the performance of the NAS in terms of delay with the generated itineraries from the MIP solution. Based on the output of the simulation, the MIP model will be modified by adjusting its parameters and solved again. This iterative process will continue until the desired result is obtained from the simulation. This paper also provides a quantitative analysis to demonstrate a trade-off between the de-peaking strategies that minimize the number of aircraft in service and the banking strategies that preserve schedule peaks.

Setting up simulation scenarios in the field of Supply Chains (SCs) is a big challenge because complex input data must be specified and careful input data management as well as precise model design are necessary. SC simulation needs a large amount of input data, especially in times of big data, in which the data is often approximated by statistical distributions from real world observations. This paper deals with the question how the model itself and its input can be effectively complemented. This takes into account the commonly known fact, that the accuracy of a model output depends on the model input. Therefore an approach for using techniques of Knowledge Discovery in Databases is introduced to derive logical relations from the data. We discuss how Knowledge Discovery would be applied, as a preprocessing step for simulation scenario setups, in order to provide benefits for the level of accuracy in simulation models.

The main goal of this study was supporting economic evaluation by assessing which new projects will be necessary and when they should be operating in order to achieve the customer service level required under a scenario of fast growth in jet fuel demand at Guarulhos terminal. In order to achieve this objective, it was built a conceptual model, data were collected and computer based model was implemented. By comparing the system’s performance under different terminal configurations, it’s possible to choose the most economical solution that performs well in different scenarios. The analysis revealed that the current infrastructure allows achieving the customer service level required until 2018. In 2019, it will be necessary to provide a solution to increase the delivery flow rate by only spending a small fraction of the previously budgeted capital expenditure, while keeping system’s performance.

The objective of inventory management models is to determine effective policies for managing the trade-off between customer satisfaction and service cost. These models have become increasingly sophisticated, incorporating many complicating factors such as demand uncertainty, finite supplier capacity, and yield losses. Curiously absent from these models are the financial constraints imposed by working capital requirements (WCR). In practice, many firms are self-financing, i.e., their ability to replenish their own inventories is directly affected not only by their current inventory levels, but also by their receivables and payables. In this paper, we analyze the materials management practices of a self-financing firm whose replenishment decisions are constrained by cash flows, which are updated periodically following purchases and sales in each period. In particular, we investigate the interaction between the financial and operational parameters as well as the impact of WCR constraints on the long-run average cost.

A heuristic approach is presented which, combined with the use of data structures, reduces storage requirements to store full optimal paths on transportation networks. The goal is to allow the use of full road networks on the implementation of simulations for the operation of a fleet of vehicles. The approach is based on the representation of routing information as a routing matrix, where we exploit structural properties of routing information to reduce storage requirements.

The multimodal freight transportation planning is a complex problem with several factors affecting decisions, including network coverage, carriers and their schedules, existing contractual agreements with carriers and clients, carrier capacity constraints, and market conditions. Day-to-day operations like booking and bidding are mostly done manually and there is a lack of decision support tools to aid the operators. These operations are governed by a complex set of business rules involving service agreements with the clients, contractual agreements with the carriers and forwarder's own business objectives. The multimodal freight transportation industry lacks a comprehensive solution for end-to-end route optimization and planning. We developed analytics for trade lane managers to identify and exploit opportunities to improve procurement, carrier selection, capacity planning, and business rules management. Our simulation based analytics tool is useful for managing business rules and for doing what-if analysis which can lead to better resource planning, cost management, and rate negotiations.

Driven by decreasing inventory storage space in stores and a corresponding need to increase delivery frequency, a major retailer is considering adding cross dock nodes, between distribution centers and stores, to its supply chain network. Currently, distribution centers serve stores directly. The retailer would like to understand if introducing an additional node allows for cost-effectively increasing delivery frequency. In the proposed scenario, the additional node would receive products from both the delivery center and upstream suppliers to serve the stores. Implemented as a discrete-event simulation, this cost-to-serve model compares the scenarios by applying costs to simulated logistics events and resource levels. Results suggest introducing new nodes is not beneficial, even considering the reduced transportation costs.

The quality of a dynamic ridesharing service is critical for commuters that usually must reach daily their end destination on time. In order to have a reasonable quality the waiting time for a ride service has to be low. This paper describes a dynamic ride sharing model proposal for commuters living in a small community in the Barcelona Metropolitan area. The proposal tries to solve the communication problems between the community and a communication hub served by train and buses. A pool has been issued to the community citizens in order to know if they are interested on the idea and willing to participate in a first test pilot. A simulation model has been built in order to decide which dynamic ride sharing model could be most appropriate given the limited numbers of answers received and the irregular mobility patterns of drivers and passengers.

Aircraft boarding is one of the critical processes affecting the turnaround time when a plane is at an airport. In this work, the aircraft boarding problem is studied with the aim of designing a boarding strategy that reduces the total boarding time considering the passengers’ behavior as well as the underlying grouping relationships among them. Our analysis suggests that some of the strategies proposed in other studies are highly theoretical and do not consider the individual characteristics of the passengers or their grouping inside the plane. This limits their applicability and validity in real operations. We propose a novel boarding strategy that is designed to reduce the total boarding time while, at the same time, aims at guaranteeing an acceptable quality of service.

Discrete event simulation (DES) techniques cover a broad collection of methods and applications that allows imitating, assessing, predicting and enhancing the behavior of large and complex real-world processes. This work introduces a modern DES framework, developed with SIMIO simulation software, to optimize both the design and operation of a complex beer packaging system. The proposed simulation model provides a 3D user-friendly graphical interface which allows evaluating the dynamic operation of the system over time. In turn, the simulation model has been used to perform a comprehensive sensitive analysis over the main process variables. In this way, several alternative scenarios have been assessed in order to achieve remarkable performance improvements. Alternative heuristics and optimization by simulation can be easily embedded into the proposed simulation environment. Numerical results generated by the DES model clearly show that production and efficiency can be significantly enhanced when the packaging line is properly set up.

Transportation of perishable products increased in volume in the last decades and imposes new challenges to today’s logistics operations. Because of the spoilage risk of the transported items, certain conditions (e.g., temperature, humidity, vibration, etc.) must be maintained during the transportation phase. Failure to maintain the required conditions may lead to product spoilage and delivery disturbances. This work considers a multi-echelon supply chain model composed of one producer and multiple customers and contrasts the performance of the logistics operations when RFID and sensing technologies are employed and when they are not. The simulation models of the two scenarios result in as much as nine different outcomes, which are presented in the form of good practice recommendations.

The paper presents some designing and organizational problems related to cross-docking terminals. These problems are defined based on literature review. The authors identify the problem of mutual arrangement of relational areas on terminal entrance and on exit as well as possible labor savings to be achieved through proper arrangement of these areas. A typical mathematical model is presented based on the literature. The paper provides also the discussion about analytical and simulation approaches to solve these problems. The authors describe how the problem of assignment areas can be solved using simulation software available on market and present a case study based on real data.

Oil refining companies and distributors often use pipelines to transport their products. In highly integrated, geographically challenging contexts, this may result in complex logistical systems. Pipelines which transport multiple products connect tanks, forming a particular, self-contained environment where distribution routes (called logistical channels), tactical inventory locations and operational criteria are defined to transfer, receive and deliver liquid oil derivatives. This paper describes a simulation model designed to represent such a regional pipeline network and includes a case study of a Brazilian region with refineries, a maritime terminal, a hub terminal and distribution bases.

This paper proposes a capacity reservation mechanism for a single-supplier and multi-manufacturer supply chain. The manufacturers first determine the production capacity they should reserve from the supplier, and then realize their reservations and place corresponding supplementary orders within the realization time window. The supplier builds its regular production capacity according to the reservations that have been received, and emergency production capacity for orders that exceed its regular capacity. Towards this end, we develop an analytical model to quantify the manufacturer’s optimal capacity reservation quantity and realization time, as well as the supplier’s optimal regular capacity. Given regular production capacity competition, a Cellular Automata (CA) simulation model is developed to resolve the analytical intractability of reservation realization time by modeling the manufacturers in an N-person game and identifying the convergence condition. Experiment results indicate that the proposed capacity reservation mechanism outperforms the traditional wholesale price contract in a decentralized supply chain.

The dynamic network loading problem is crucial for performing dynamic traffic assignment. It must reproduce the network flow propagation, while taking into account the time and a variable traffic demand on each path of the network. In this paper, we consider a simulation-based approach for dynamic network loading as the best-suited option. We present a multiclass multilane dynamic network loading model based on a mesoscopic scheme that uses a continuous-time link-based approach with a complete demand discretization. In order to demonstrate the correctness of the model, we computationally validate the proposed simulation model using a variety of laboratory tests. The obtained results look promising, showing the model's ability to reproduce multilane multiclass traffic behaviors for medium-size urban networks.

The relative novel discrete rate-based simulation paradigm combines the advantages of the discrete event-based and the system dynamics simulation paradigms. Although its applicability is generally acknowledged in the context of supply chain management, no research works exist, that allow for a direct modeling and simulation of supply chain planning decisions within this paradigm. This paper therefore presents necessary adaptations of the discrete rate-based simulation paradigm for tactical supply chain planning decisions. Our main research contribution lies in extending the discrete rate-based simulation with modeling and material flow controlling mechanisms for enabling a simple implementation and simulation of tactical supply chain planning tasks. To evaluate different planning decisions in this context a multitude of simulation runs are necessary, creating a need for fast simulation approaches. Thus, we show formally, that discrete rate-based simulation models can generally be computed faster than commonly used discrete event-based simulation models.

The following paper describes an overall simulation method for the design of eMobility concepts for rail, road and coastal waters. The presented workflow enables the user to execute the realization process under the usage of real world elevation data and driving profiles. This is essential for the choice of a fitting battery and motorization. Additionally the charging infrastructure has to be chosen without changing anything on the existing driving schedule for electric buses. Taking a look at innovative and sustainable drive concepts completes the overview and indicates the direction for the implementation of eMobility in the near future without the need for big investments. With the shown software approach it is possible to answer the consumers cost questions and reduce their range anxiety for electric vehicles.

Due to rapid urbanization, large cities in developing countries have problems with heavy traffic congestion. International aid is being provided to construct modern traffic signal infrastructure. But often such an infrastructure does not work well due to the high operating and maintenance costs and the limited knowledge of the local engineers. In this paper, we propose a frugal signal control framework that uses image analysis to estimate traffic flows. It requires only low-cost Web cameras to support a signal control strategy based on the current traffic volume. We can estimate the traffic volumes of the roads near the traffic signals from a few observed points and then adjust the signal control. Through numerical experiments, we confirmed that the proposed framework can reduce an average travel time 20.6% compared to a fixed-time signal control even though the Web cameras are located at 500 m away from intersections.

Right-of-way prioritization at unsignalized intersections has been largely unexplored. Drivers do not always use consistent methods to determine who has the right-of-way to enter the intersection at unsignalized intersections. Problems with right-of-way assumptions include that not all drivers engage in one set algorithm to assess intersections priority, and issues of yielding can occur when drivers arrive at an intersection simultaneously or near-simultaneously. A discrete event simulation model was built to emulate a 4-way stop-signed intersection; and different prioritization rules were instated to determine which lane has right-of way. First-in-first-out and yield-to-right prioritization methods were found to differ in terms of time spent waiting and traveling through the intersection, as well as intersection throughput for different intervals of high traffic volume. The first-in-first-out prioritization algorithm provided superior service to drivers arriving at an intersection, compared to the traditional yield-to-right approach, in both low- and high-traffic volume conditions.

In this paper, we consider the modeling and simulation of low-volume mixed model assembly lines that can be found in the aerospace industry. Low-volume mixed model assembly processes are characterized by a large amount of tasks to be manually performed, buffer space constraints, specialized resources like jigs and tools, and a large number of external suppliers. The main principles of modeling and simulating such manufacturing systems are discussed. Based on a domain analysis, the major building blocks of simulation models for low-volume mixed model assembly lines are derived. We exemplify their implementation using the commercial discrete-event simulation tool AutoSched AP. As an application of these building blocks, we analyze the cabin installation process in a final assembly line in aircraft production using discrete-event simulation.

A U-shaped production line is considered one of the most flexible designs used by companies to adapt to varying production conditions and to implement lean concepts. Similarly, work-sharing allows for cross-training of a flexible workforce while achieving high levels of worker utilization. This paper proposes a new protocol for U-shaped assembly lines that relies on work-sharing principles and on an adaptation of bucket brigades to cellular environments. Discrete event simulation is used to maximize throughput while determining buffer locations and buffer levels for each worker. This model is validated with a physical simulation and then tested with industry data. The results show the protocol enables a high level of throughput and worker utilization for the manufacturing cell while capping the maximum amount of WIP in the system. The proposed protocol is generalizable with respect to the number of stations, processing times, types of processes, and worker velocities.

We consider an assemble-to-order production system where the product demands and the time since the last customer arrival are not independent. The simulation of this system requires a multivariate input model that generates random input vectors with correlated discrete and continuous components. In this paper, we capture the dependence between input variables in an undirected graphical model and decouple the statistical estimation of the univariate input distributions and the underlying dependence measure into separate problems. The estimation errors due to finiteness of the real-world data introduce the so-called input uncertainty in the simulation output. We propose a method that accounts for input uncertainty by sampling the univariate empirical distribution functions via bootstrapping and by maintaining a posterior distribution of the precision matrix that corresponds to the dependence structure of the graphical model. The method improves the coverages of the confidence intervals for the expected profit the per period.

This paper presents the use of simulation based optimization in addressing manufacturing flow problems at a heavy equipment manufacturer. Optimizing the buffer allocation in an assembly line and optimizing the worker assignment at workstations are two independent problems addressed, with the objective to maximize throughput rate. The simulation models of the system, built using an in-house tool based on SLX, is interfaced with an custom designed meta-heuristic based on Particle Swarm Optimization (PSO). Two versions of the PSO have been developed: one with integer decision variables (for buffer space allocation) and another with binary variables (for worker assignment). The performance of the proposed simulation based optimization scheme is illustrated using case studies.

This study presents a simulation design and analysis case study of a flexible manufacturing system (FMS) considering a multi-response simulation optimization using TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) based Taguchi approach. While in order to reduce expensive simulation experiments with the Taguchi design, the TOPSIS procedure is used to combine the multiple FMS responses (performance measures) into a single response in the optimization processes. Thus, TOPSIS carries out an important role to build a surrogate objective function that represents multiple responses of the system. The integrated approach finds a new design considering discrete factors (physical and operational parameters) which affect the performance measures of FMS. Optimal design configuration is obtained for the considered system with improved performance.

In a flat panel display (FPD) production line, unlike a table-type machine that processes one glass at a time, an inline cell works simultaneously on several glasses unloaded from different cassettes in a serial manner and is divided into two types (uni-inline cell and bi-inline cell) according to the job loading and unloading behavior. In order to build a production simulator for this type of FPD production line, an object-oriented event graph modeling approach is proposed where the FPD production line is simplified into a job shop consisting of two types of inline cells, and the job shop is represented as an object-oriented event graph model. This type of job shop is referred to as a heterogeneous job shop. The resulting model is realized in a production simulator using an object-oriented event graph simulator and is illustrated with the experimental results from the production simulator.

Machine failures are major causes of direct downtime as well as system losses (blocked and idle times) in production flows. A previous case study shows that prioritizing bottleneck machines over others has the potential to increase the throughput by about 5%. However, the bottleneck machine in a production system is not static throughout the process of production but shifts from time to time. The approach for this paper is to integrate dynamic maintenance strategies into scheduling of reactive maintenance using Discrete Event Simulation. The aim of the paper is to investigate how a shifting priority strategy could be integrated into the scheduling of reactive maintenance. The approach is applied to and evaluated in an automotive case-study, using simulation for decision support. This shows how to shift prioritization by tracking the momentary bottleneck of the system. The effect of shifting priorities for planning maintenance activities and its specific limitations is discussed.

For small-volume products in particular, U-shaped assembly systems represent an important alternative to straight-line systems. For this kind of system, staff assignment can take various forms: assignment to adjacent and opposing stations, mixed and one-piece flow assignment. In this paper, a U-shaped assembly system is defined on the basis of a known straight-line assembly, and the most suitable form of staff assignment is then determined. If the station layout is interpreted as a capacity graph, and if staff assignment is defined in the form of a staff assignment graph, the same methods can be used to solve the staff assignment problem as those used to match the precedence and capacity graphs for the purpose of line balancing. It is shown that the performance of simulated solutions sometimes varies greatly from that of static balancing solutions, in particular if staff travel times are taken into account.

Manufacturing organizations are able to accumulate large amounts of production and environmental data due to advances in data collection, communications technology, and use of standards. The challenge has shifted from collecting a sufficient amount of data to analyzing and making decisions based on the huge amount of data available. Data analytics (DA) can help understand and gain insights from the big data and in turn help advance towards the vision of smart manufacturing. Modeling and simulation have been used by manufacturers to analyze their operations and support decision making. This paper proposes multiple methods in which simulation can serve as a DA application or support other DA applications in manufacturing environment to address big data issues. An example case is discussed to demonstrate one use of simulation. In the presented case, a virtual representation of machining operations is used to generate the data required to evaluate manufacturing data analytics applications.

In production planning, one of the major challenges for plan optimization lies in quantifying the dependence of the objective criterion (typically total cost) upon the decision variables that specify a release plan of jobs. Existing methods either fall short in capturing such a relationship, which involves non-stationary stochastic processes of a manufacturing system (e.g., the number of jobs over time), or require discrete-event simulation (DES) to evaluate the objective criterion for each candidate decision, which is time-consuming. To enable the accurate and precise estimation of the objective for any decision plan within a reasonable time, this work proposed a metamodeling-based approach. The metamodels take the form of difference equations, embody the high-fidelity of DES, and can be used to address "what if" questions in a timely manner. When embedded in the optimization of production planning, the metamodels can help to improve the quality and responsiveness of decision making.

Job shops produce products on the basis of manufacturing orders which specify the due date and the volume. The orders accepted by the shop floor are put into a job pool. The job release decides when to start each job in the pool. It attempts not only to balance this time-varying demand against available capacity, but also manages to meet the due date constraints. The general job release policies, such as output-based or workload-based policies, have poor due date performance. A multi-time-periods release policy is proposed to match the time-varying demand. The due date pressure is distributed to every period. In each time period a near optimal short-term throughput of each product is obtained by an optimization model. The optimization problem is solved by an improved ant colony algorithm. In iteration processes of the algorithm ants are evaluated by the simulation which involves the setup and breakdown of machines.

A hierarchical production planning structure enables manufacturing systems to handle customer disturbances with different measures on different planning levels. Two different kinds of customer order behavior can be observed and are as well discussed in literature. In the first, being forecast-evolution-behavior, customers provide a forecast quantity for a specific due date for a long horizon in advance and update their forecast quantities periodically. In the second, being customer-required-lead-time-behavior, customers demand stochastic order amounts with a customer-required-lead-time whereby the manufacturing company generates an aggregated forecast, e.g., for product groups. These required lead times are usually shorter than the forecast-evolution-horizon, but order quantities do not further change. For comparing the influence of both order behaviors on a hierarchical production planning system, a simulation study is performed in which logistic performance measures such as service-level, utilization, capacity-, inventory- and tardiness-costs are analyzed with respect to a normalized forecast quality measure.

Production planning in the Semiconductor Industry (SI) has emerged as the most complex process due to its process complexity, technological constraints and high-mix low-volume production characteristics. In this paper, we present two different production planning approaches, developed by STMicroelectronics and G-SCOP research laboratory, to better control the production in 300mm production line at Crolles. At first, a mixed integer program (MIP) is proposed that projects the production lot trajectories (start and end dates) for the remaining subsequent steps, taking into account finite production capacities. A heuristic is then proposed to simplify the problem of finite capacity by neglecting equipment capacity. This approach results in the development of an infinite capacity WIP projection engine that complies with lots due dates and takes into account cycle time variability.

Production-inventory systems model the interaction of manufacturing processes with internal and external customers. The role of inventory in these systems is to buffer mismatches between production and demand caused by process uncertainty. Often, production and demand variability is described using simplified probabilistic models that ignore underlying characteristics such as skewness or autocorrelation. These models lead to suboptimal inventory policies that result in higher costs. This work presents a novel analysis of the impact of uncertainty in the performance of production-inventory systems. It quantifies the effect of different probabilistic descriptions of production capacity and demand in systems subject to lost sales or backorders. The analysis is based on the results of discrete-event simulations. The flexibility offered by simulation allows studying diverse conditions that arise in production-inventory systems. The results clearly illustrate the importance of appropriately quantifying variability and performance measures for inventory management in process networks.

This paper presents a case study that validated the production capacity of an industrial batch chemical process. During a risk assessment review of the production system, subject matter experts identified that different constraints and uncertainties could limit the actual production capacity of the plant to less than designed. The determine if production capacity was a risk, we developed a discrete event simulation to simulate a batch chemical production process that was characterized by presence of multiple parallel production units, interlocks in product loading steps, uncertainty in processing times caused by failures, degradation of production process over time, and planned maintenance shutdowns. We evaluated the impact of variation in the degradation rate of the production process, and the impact of changes in renewal frequency on the total production capacity of the plant.

We simulationists are in the meta business. The best of us think in abstracted terms about the questions we are tasked to answer and the referents we are challenged to represent. Yet what we have found most elusive is the meta-language for our own identity. We have been called "tool builders" but we know we are more. Our position descriptions are entitled "engineer, scientist, programmer, analyst, researcher" but we know those words are inadequate. Our terminology overlaps with the ontology owned by the systems we simulate. Our attempts to be officially recognized as a unique profession have thus far failed. But we recognize something in ourselves and in each other that we have thus far failed to name. Literal attempts to differentiate us from other technical professionals have proven uninspiring. Is it possible for us to apply our metaphorical skills to unite our thinking and equip us with a common vision? Or are we simply very like simulationists?

The U.S. Air Force aircraft maintenance depots face complex operating environments due to the diversity of aircraft or mission design series (MDS) maintained by each depot and the variability of maintenance requirements for each MDS. Further complicating their operations is the variability of maintenance actions required from one aircraft to another within each MDS and a highly specialized workforce that has inherent inflexibility to compensate for the workload variability. Air Force Materiel Command is reviewing maintenance personnel multi-skilling as a method to efficiently absorb the variability of workload and maintenance requirements between aircraft. Using a simulation built in ARENA 14®, we studied the F-22 Heavy Maintenance Modification Program through a series of designed experiments. Our study analyzes whether using a multi-skilled workforce impacts the productivity of depot maintenance personnel through simulation of several multi-skilling policies.

Given constraints dictated by the current fiscal environment, the Army has been directed to reduce its total personnel strength from around 1.05 million across the active duty, Army National Guard, and Army Reserve, to a maximum of around 980 thousand personnel. In particular, the active duty Army will have to drawdown to around 450 thousand personnel. In this paper we discuss a methodology the Army is using to help inform decisions about how to execute this drawdown. We describe a simulation-based optimization that identifies potential cuts to a large subset of the active duty Army’s total strength.

Designing an Army force structure - the set of equipment, personnel, and skills that define the US Army - consists of a daunting set of interacting problems. Such analyses must deal with a wide range of force structure decisions, uncertainty about the future, and account for dynamics between force structure decisions. Recent methodologies at CAA use random variables for Army force structure demands. Due to constraints and dependencies, the business rules for determining a valid demand signal require more than simple draws from canonical distributions. Further, the rule-set must be open to extension to incorporate evolving sponsor constraints. Helmet is a novel Domain Specific Language (DSL) for defining complex demand sample generators. Implemented in the Clojure programming language, Helmet provides a robust, extensible platform for building stochastic force structure demands.

Computer simulation models have been used for many years to assess the overall effectiveness of a military Campaign. At the Campaign level, engagements (such as air-to-air combat) will invariably be represented at relatively high levels of aggregation. This paper explores the potential for using a ranked outcome approach (rather than a traditional probabilistic approach) to provide an alternative representation of engagements within an air combat simulation.

Man-Portable Air-Defense System (MANPADS) missiles are threats to military aircraft. Analytical models are used to help design military aircraft to survive a variety of attacks, including those from Man-Portable Air-Defense Systems. These models need accurate fragment capture data consisting of the fragment size and velocity resulting from weapon detonation. Accurate data require accurate testing which in turn requires effective test design infrastructure. We model this test infrastructure. MANPADS missiles are detonated within test arenas that have make-screens placed on the arena walls to capture fragment impact data. Our model mimics the test process and provides a quantitative metric with which to examine and compare test arena configurations. We overview our model and quality metric and offer a case study in which these are used to find a robust arena make-screen configuration.

This paper considers the decoy location problem, i.e., the problem of determining optimal locations for decoys that protect a surface-based radar against an anti-radiation missile. The objectives of the problem are to simultaneously maximize distances between the missile's detonation point and the radar as well as the decoys. The problem is solved using a stochastic simulation model providing the distances as well as a ranking and selection procedure called MOCBA-p. In the procedure, location combinations are evaluated through a multi-attribute utility function with incomplete preference information regarding weights related to the objectives. In addition, multi-objective computing budget allocation is used for allocating simulation replications such that the best combinations are selected correctly with high confidence. Numerical experiments presented in the paper illustrate the suitability of MOCBA-p for solving the decoy location problem. It provides computational advantages over an alternative procedure while also enabling ease of determining the weights.

The idea of the magic circle, frequently referenced in the serious gaming community, is explained and then shown to be a possible perspective to view live-virtual-constructive (LVC) simulation federations. This view opens the method of categorizing different roles and data capabilities for the various elements and actors within an LVC federation. The applicability of this view is shown in some explanatory detail, including a description of its applicability to simulations, the different types of roles, and the variety of knowledge (procedural and propositional) that can be qualified when the federation is viewed this way. Structured identification of the federation elements and the data they are exchanging is relied on to describe peculiar data interoperability issues that exist within current LVC federation architectures, and which may also affect future LVC federation architectures currently under development.

Situational awareness (SA) information in tactical mobile ad hoc networks (MANETs) is essential to enable commanders to make informed decisions during military operations. Sharing SA information in MANETs is a challenging problem because missions are run with dynamic network topologies, using unreliable wireless links, and with devices that have strict bandwidth and energy constraints. Development and validation of efficient data delivery methods in MANETs often require simulation; however, the literature is sparse regarding simulations specifically for SA dissemination. In this paper we present a simulation implementation for a newly proposed Opportunistic SA Passing (OSAP) scheme and investigate its efficiency in realistic scenarios. Moreover, we propose several metrics aimed at facilitating evaluation of SA dissemination schemes in general, and we demonstrate the applicability of the metrics in our simulation results. Our simulation provides a flexible framework and evaluation platform for experimental studies of SA data dissemination in tactical MANETs.

Data Farming is a process that has been developed to support decision‐makers by answering questions that are not currently addressed. It uses an inter‐disciplinary approach that includes modeling and simulation, high performance computing and statistical analysis to examine questions of interest with large number of alternatives. Data Farming allows for the examination of uncertain events with numerous possible outcomes and provides the capability of executing enough experiments so that both overall and unexpected results may be captured and examined for insights. In 2010, the NATO Science and Technology Organization started the three‐year Task Group “Data Farming in Support of NATO” to assess and document the data farming methodology to be used for decision support. Two case studies were performed as proof‐of-concept explorations to demonstrate the power of Data Farming. The paper describes the Data Farming methodology as an iterative process and summarizes the results of the case studies.

Big data analytics is at the brink of changing the landscape in NXP Semiconductors Back End manufacturing operations. Numerous IT tools, implemented over the last decade, collect gigabytes of data daily, though the potential value of this data still remains to be explored. In this paper, the software tool called Heads Up is presented. Heads Up intelligently scans, filters, and explores the data with use of simulation. The software provides real-time relevant information, which is of high value in daily, as well as long term, production management. The software tool has been introduced at the NXP high volume manufacturing plant GuangDong China, where it is about to shift the paradigm on manufacturing operations.

This paper aims to shed some light on how the concept of cloud manufacturing has been applied to the semiconductor manufacturing operations. It starts with describing the challenges to the semiconductor manufacturing due to evolving of outsourcing business model in global context, then discusses the different forms of cloud manufacturing and proposes the semiconductor industry oriented architecture for cloud manufacturing. Serus is used as a case study to share how the cloud manufacturing has created the values for the customer and its outsourced suppliers in the semiconductor industry.

Diversified and complicated manufacturing sites make optimal scheduling of production lines difficult. Under current manufacturing processes, it is almost impossible for schedulers to consider all the constraints of production processes. A strategy of simulation-based APS is employed to overcome difficulties that interfere with satisfactory on-time delivery and commitment to the current status. In simulation-based scheduling, KPIs are important for selecting optimal dispatching rules in scheduling. In cases involving complex processes, in which the identification of appropriate KPIs is limited to selection among existing KPIs, KPIs should be chosen and modified carefully to optimize process management and to reflect all of the existing constraints of production. However, the existing methodologies for modifying KPIs are misplaced in complex manufacturing environments such as job-shop processes. We propose a method to design and select KPIs meeting the characteristics of given process, and verify empirically whether the KPIs meet requirements from experts of production lines.

Changeover setups are induced by switching manufacturing processes among products. They commonly exist in flexible manufacturing systems. Modeling their queue time impact correctly is of fundamental importance in evaluating the performance of production systems. In this paper, the mean queue time approximation models are proposed based on the properties of changeover setups. The models are validated by simulations and perform well in the examined cases.

This paper proposes using data analytic tools to generate operating curves in complex systems. Operating curves are productivity tools that benchmark factory performance based on key metrics, cycle time and throughput. We apply a machine learning approach on the flow time data gathered from a manufacturing system to derive predictive functions for these metrics. To perform this, we investigate incorporation of detailed shop-floor data typically available from manufacturing execution systems. These functions are in explicit mathematical form and have the ability to predict the operating points and operating curves. Simulation of a real system from semiconductor manufacturing is used to demonstrate the proposed approach.

Measuring Cycle Time gains was not consistent in Semiconductor manufacturing Fab. A method was required to standardize how to measure Cycle Time, and that could measure Cycle Time irrespective of the nodes being built at manufacturing Fab. Once Cycle Time can be measured consistently from manufacturing Fab, project could be developed to reduce Fab Cycle Time and measured to verify results at the completion of the project. This paper will show the steps taken to develop a standardized method of measuring Cycle Time through the use of the Queuing Theory formula (G/G/m), where the Queuing Theory Formula Parameter data was collected from, how the Cycle Time data was validated for accuracy, how the Cycle Time data was used to identify areas of improvement, and how the completed Cycle Time project data was feed back into the Queuing Theory formula for final validation.

Index terms: Queuing Theory, Cycle Time, Queuing Parameters

We consider a semiconductor production line in which production stations are afflicted by a defect deposition process and immediately followed by an inspection step. We propose to integrate operational aspects into quality considerations by formulating a Cycle Time (CT) versus Yield trade off. We connect the two performance measures through the determination of the limit for defects at the inspection step. We extend former results to a tandem production line and present an optimal greedy algorithm that provides the Pareto-optimal set of Upper Control Limit (UCL) values for the line. The obtained model enables decision makers to knowingly sacrifice Yield to shorten CT and vice versa.

Variability is an inherent component of all production systems. To prevent variability propagation through the whole production line, variability must be constantly monitored, especially for bottleneck toolsets. In this paper, we propose measures to evaluate workload variability for a toolset configuration. Using industrial data, we show how making the toolset configuration more flexible by qualifying products on machines decreases variability. By quantifying the toolset workload variability, our variability measures makes it possible to estimate the variability reduction associated to each new qualification. The industrial results show significant workload variability reduction and capacity improvement.

Material flow forecast based on Short-Term Simulation has been established as a decision support solution for fine-tuning of Preventive Maintenance (PM) timing at Infineon Dresden. To ensure stable forecast quality for effective PM decision making, the typical tool uptime behavior needs to be portrayed accurately. In this paper, we present a hybrid tool down modeling approach that selectively combines deterministic and random down time modeling based on historical tool uptime behavior. The method allowed to approximate the daily uptime of reality in simulation. A generic framework to model historical down behavior of any distribution type, described by the two parameters Mean Time to Failure (MTTF) and Mean Time to Repair (MTTR) is also discussed.

This paper presents an approach for scheduling different types of preventive maintenances (PMs) for a work center of a semiconductor manufacturing facility. The PM scheduling problem includes time-dependent synchronization constraints and is implemented in a constraint programming model. A mix of periodic and workload-specific maintenances is scheduled considering the synchronization to available engineers which have individual shift schedules and skills that define the range feasible maintenances. This also comprises maintenances having process durations covering multiple shifts, which requires a continuous availability of sufficiently skilled engineers. Additionally to the PMs, also handling and maintaining of unscheduled downs is considered in the model. Multiple objectives are investigated and used for optimization and tested on realistic data.

In semiconductor manufacturing, preventive maintenance (PM) activities are typically scheduled via a two tier hierarchical decomposition approach. The first decision tier determines a PM cycle plan while the second tier schedules these planned events into the manufacturing operations. Following recent work based on the use of G/G/m queueing approximations for PM planning, we develop a method to allow for multiple PM cycles in a tool set. We formulate a nonlinear program with the PM cycle durations as continuous decision variables with the objective of minimizing the mean cycle time. We examine certain special cases and characterize the optimal solutions. Numerical studies are conducted with realistic multiple PM cycle data to assess the implications of the proposed approach. The results suggest that it may be possible to obtain significant improvements in the overall cycle time performance of tool sets in semiconductor manufacturing relative to existing PM planning procedures.

Scheduling decisions have an important impact on the overall performance of a semiconductor manufacturing facility (fab). To account for machines that consist of several interdependent components, we generalize the flexible job-shop scheduling problem. We introduce the concept of route graphs to describe resource dependencies. Beside specifying feasible routes, route graphs can, for example, prescribe two different operations in the route of a job to use the very same resource. To solve the problem, we introduce an adapted disjunctive graph representation and propose a heuristic method that iteratively inserts jobs to construct an initial solution. This solution is then improved using a simulated annealing meta-heuristic. Several numerical experiments are performed. First, improved results for a real-world instance justify the increased complexity of our model. Second, a comparison to results of dedicated methods for the flexible job-shop scheduling problem shows that our approach obtains good results.

In this paper, we discuss a two-stage flow shop scheduling problem with batch processing machines. The jobs belong to different incompatible families. Only jobs of the same family can be batched together. The performance measure is the total weighted tardiness of the jobs. A decomposition heuristic is proposed that is based on the idea to iteratively determine due dates for the jobs in the first stage and earliest start dates of the jobs in the second stage. The two resulting subproblems are solved using a time window decomposition (TWD) heuristic and a variable neighborhood search (VNS) scheme. Results of computational experiments based on randomly generated problem instances are presented. We show that the VNS-based scheme outperforms the TWD heuristic. In addition, we show that the decomposition scheme can be parallelized. As a result, the amount of computing time is modest, even for the computational expensive VNS scheme.

Efficiently managing the production speed of multiple competing products in semiconductor manufacturing facilities is extremely important from the line management standpoint. Industries have exploited the real time dispatching (RTD) to cope with the problem for the last decade, but the top tier companies have started looking at modern scheduling techniques based on mathematical modeling. We provide real-time scheduling based on mixed integer programming (MIP) capturing the salient characteristics such as shift production targets, machine dedication, sequence-dependent setups, foup queue time, foup priority, schedule stability, etc. Then the reason of specific sequence of foup schedule is communicated to the floor through a self-expository Gantt-Chart. The computer code is written in ezDFS/OPL which provides an all-in-one environment of data manipulation, optimization model development, solving, post processing, and visualization.

Over the last three decades an extensive research literature on modelling and simulation applications in the semiconductor industry has developed. Extensive relationships between academic research groups and industry have also been in evidence. This presentation will briefly review the evolution of modelling and analysis research in semiconductor manufacturing from an academic perspective, discuss its implications for academic research and industrial practice, and suggest a number of future directions for research and for more effective industry-university collaboration.

Large, fine-grain data collected from an actual semiconductor supply-demand system can help automated generation of its integrated simulation and optimization models. We describe how instances of Parallel DEVS and Linear Programming (LP) models can be semi-automatically generated from industry-scale relational databases. Despite requiring the atomic simulation models and the objective functions/constraints in the LP model to be available, it is advantageous to generate system-wide supply-demand models from actual data. Since the network changes over time, it is important for the data contained in the LP model to be automatically updated at execution intervals. Furthermore, as changes occur in the models, the interactions in the Knowledge Interchange Broker (KIB) model, which composes simulation and optimization models, are adjusted at run-time.

Simulation is a widely used technique for analyzing and managing supply chains. Simulation software packages offer standard libraries for selected functions and application areas. However, no commercial or freeware simulation tool proposes building blocks specific to semiconductor manufacturing. Thus, we propose in the present paper a library with a collection of simulation objects that can be used to model supply chain activities of various scales in the semiconductor industry. The library denoted by SCSC-SIMLIB strives for reducing modeling effort. It also enables standardization and benchmarking. We first describe the requirements for such a library and we suggest an architecture. Then, selected objects of the library are presented in more detail. Finally, we demonstrate the benefits of SCSC-SIMLIB, which is implemented by means of the simulation software AnyLogic, with a use case based on a simple example.

This paper introduces a new modified version of scheduling approach,Control Point Policy (CPP) for semiconductor wafer fabrication lines and compare its performance with popular scheduling rules, including Earliest Due Date (EDD), Minimum Slack (MS) and Critical Ratio (CR). A discrete event simulation is constructed to evaluate the performance of CPP for three important performance parameters for semiconductor fabrication lines; cycle times, waiting times and inventory levels. New insights for system performance are developed with the implementation of CPP at bottleneck stations and the introduction of finite size buffers between all the workstations in the semiconductor fab line. Our simulation results demonstrate the ability of CPP to achieve lowest cycle time with minimum inventory levels for situations where products with similar due dates are prioritized over each other. CPP is found to give good system performance for environments where multiple products at different processing stages compete for limited resources.

Presented in this paper is a dispatching rule to achieve the on-time delivery for an order-driven FAB involving high priority orders. We classify orders into two groups; regular orders (RO), high priority orders (HPO). HPO lots have shorter cycle times, tighter target due date and higher margins than RO lots. The proposed rule introduces the concept of reservation for HPO lots, which means the provisional allocation of capacity for on-time delivery of HPO lots. Since the rule considers the due date of HPO lots first, RO lots might be tardy. To minimize the tardiness of RO lots, the proposed rule takes into account tool utilization as well as on-time delivery of HPO lots. We developed a simulation model based on MIMAC6, and conducted experimentations with MOZART®. The experimentation results show that the proposed dispatching rule can achieve the on-time delivery of HPO lots with the minimum tardiness of RO lots.

Manufacturing is today often characterized by a growing number of customer-specific products that have to be manufactured and delivered in given lead times, according to concrete delivery dates. Thus, highly relevant questions like “When to start a production order at latest, in order to stay within my lead time?” are answered by more or less primitive, backward-oriented planning approaches and without taking into consideration uncertainty or alternatives. It gets more complex, if different products are to be produced and the more complex the underlying manufacturing system is (e.g., semiconductor with re-entry cycles). These questions could be answered more specifically, more detailed and more robust, if discrete, event-based simulation (DES) would be applied in a backward-oriented manner. This paper describes evaluation results from the semiconductor domain and names restrictions and limits. They also show, that the backward-oriented simulation approach can be applied successfully for the scheduling of customer-specific orders.

This paper provides an analysis of the impact, on the capacity, of decisions that are taken when sequencing tasks on machines in an industrial workshop. This study have been led on a particular workshop of a microelectronics plant. Such area is well-known in semi-conductor manufacturing to be very complex to schedule and subject to many constraints (external resources, sequence-dependent setup times, lot families, etc.). Hence, this paper also contains a comparison, on different heuristics, of the capacity when some of these constraints are removed.

As an important and challenging problem, the scheduling of semiconductor manufacturing is a hot topic in both engineering and academic fields. Its purpose is to satisfy production constraints on both production process and resources, as well as optimizing some performance indexes like cycle-time, movement, etc. However, due to its complexities, it is hard to describe the scheduling process with a mathematical model, or to use conventional methods to optimize its scheduling problem. A Simulation approach is proposed to optimize the scheduling of a semiconductor manufacturing system, i.e., a simulation-based optimization (SBO) approach. Because the high computational cost of SBO approach could hinder its application in the real production line, a parallel/distributed architecture is discussed to improve its efficiency. Using genetic algorithm (GA) as an optimization algorithm, the proposed parallel-SBO based scheduling approach for semiconductor manufacturing system is tested for its feasibility and effectiveness.

Developing dispatching rules for complex production systems such as semiconductor manufacturing is an involved task usually performed manually. In a tedious trial-and-error process, a human expert attempts to improve existing rules, which are evaluated using discrete-event simulation. A significant improvement in this task can be achieved by coupling a discrete-event simulator with heuristic optimization algorithms. In this paper we show that this approach is feasible for large manufacturing scenarios as well, and it is also useful to quantify the value of information for the scheduling process. Using the objective of minimizing the mean cycle time of lots, we show that rules created automatically using Genetic Programming (GP) can clearly outperform standard rules. We compare their performance to manually developed rules from the literature.

The high variety of and intermittent demand for semiconductor memory products frequently limits the use of forecast error normalization in estimating inventory. Inventory turnover is a practical performance indicator that is used to calculate the number of days for which a company retains inventory before selling a product. Although previous studies on inventory level settings have primarily applied information regarding demand variability and forecast error, few studies have investigated the inventory turnover for inventory decisions. Inventory turnover data are time scaled, suited for a small sample, and right censored to fit the input of survival analysis. In this study, a model in which inventory turnover and survival analysis were integrated was developed to estimate the production inventory survival function used to determine inventory level. Data analysis results based on real settings indicated the viability of using inventory survival analysis to determine semiconductor memory inventory level settings.

In semiconductor manufacturing, early life failures have to be screened out before delivery. This is achieved by means of burn-in. With the aim to prove a target reliability level and release burn-in testing of the whole population, a burn-in study is performed, in which a large number of items is investigated for early life failures. However, from a statistical point of view, there is substantial potential for improvement with respect to the modeling of early life failure probabilities by considering further available information in addition to the performed burn-in studies. In this paper, we provide ideas on how advanced statistics can be applied to efficiently reduce the efforts of burn-in studies. These ideas involve scaling the failure probability with respect to the sizes of the different products, as well as taking advantage of synergies between different chip technologies within the estimation of the chips' failure probability level.

In this study, fatigue life of power semiconductor devices is measured in cycles to failure during an accelerated stress test in a climate chamber. The tested devices fail mainly in a short circuit event and their physical inspection reveals cracks in the power metallization. Commonly, the time till fracture of macroscopic metal layers is modeled with S-N or e-N fields, this means that the lifetime (N) depends on the mechanical stress (S) or the strain (e), respectively. Metal layers of semiconductor devices are microscopic (≤20um) and, in general, their ageing mechanisms are different than for macroscopic layers, nevertheless the application of the macroscopic based e-N model to semiconductor lifetime data shows good results. The parameter estimates are not only mathematically but also physically plausible, which indicates that fatigue life due to micro-mechanisms can be described by parameters representing the mechanical load (strain) in the device.

This paper provides a modeling approach for dealing with the challenging question of trade-off between capital utilization and production efficiency in semiconductor manufacturing where the ultimate goal is of maximum output at maximum velocity and minimum cost. Full fab simulation is used iteratively between models of “horizontal” and “vertical” simulations in order to rapidly generate results for different possible states of the fab with varying capital costs, matched cycle time (CT), and fixed throughput rate, so as to determine the most efficient operating condition for the fab with respect to cost, CT, and output.

To successfully manufacture logic and power semiconductors in existing high mix semiconductor factories, fast ramp up phases and frequent product changes are necessary. Especially for power semiconductor production, new manufacturing and automation concepts are required, e.g., regarding the use of other substrates than silicon wafers. To allow a judgment on how an existing automated material handling system (AMHS) can cope with the new challenges or which alterations are required, a material flow simulation is essential. However, the planning and creation of such simulation experiments is difficult because of the systems complexity, the large amount of boundary conditions and the effort of manually modifying and testing many different variants, partially using currently unforeseen automation concepts. In order to assist in this process, the authors suggest a method for rapidly creating valid simulation experiments using an ontology that allows for the reuse of previous experiments and system experts knowledge.

With the speedup of product innovation, product life cycles are becoming shorter and shorter. To fully support customers, a foundry fab has to speed up its wafer-output schedule to shorten the time to market in the growth stage. High priority setting and small lot strategy are common techniques for accelerating manufacturing cycle time. However, small lot strategy would accompany 1.2% of extra equipment investment. The improvement should be sufficient so that the extra investment is worthy. Therefore, the novel strategy, dedicated line for small lot size manufacturing, is proposed in this paper, which allocates exclusive resources for expediting lots to greatly improve the manufacturing cycle time. By distinguishing dedicated lines from regular lines, the proposed strategy improves 21.7% of cycle time for expediting lots. Plenty of simulation results can evidence that the proposed strategy significantly outperforms mixed run mode.

In this article, the relationships between virtual metrology and actual measurements are investigated with respect to a sampling decision system (SDS); specifically, a multilevel Virtual Metrology strategy is relied on to provide predictive information. Such virtual measurements serve as input for the sampling decision system, which in turn suggests the optimal measurement strategy. Two approaches relying on decision-theoretical concepts are discussed: the expected value of measurement information (EVofMI) and a two stage sampling decision model. The basic assumption of the SDS-VM system is that it is not necessary to perform a real measurement until it is strictly needed. The two methodologies are then validated relying on simulation studies and actual chemical vapor deposition (CVD) process and measurement data. The ability of the proposed system to sample dynamically the wafer measurements in dependence of the calculated risk is then evaluated and discussed.

Reliable semiconductor devices are of paramount importance as they are used in safety relevant applications. To guarantee the functionality of the devices, various electrical measurements are analyzed and devices outside pre-defined specification limits are scrapped. Despite numerous verification tests, risk devices (Mavericks) remain undetected. To counteract this, remedial actions are given by statistical screening methods, such as Part Average Testing and Good Die in Bad Neighborhood. For new semiconductor technologies it is expected that, due to the continuous miniaturization of devices, the performance of the currently applied screening methods to detect Mavericks will lack accuracy. To meet this challenge, new screening approaches are required. Therefore, we propose to use a data transformation which analyzes information sources instead of raw data. First results confirm that Independent Component Analysis extracts meaningful measurement information in a compact representation to enhance the detection of Mavericks.

In automotive industry end-of-life tests are necessary to verify that semiconductor products operate reliably. Due to limited test resources it is not possible to test all devices and thus, accelerated stress tests in combination with statistical models are commonly applied to achieve reliable forecasts. Challenging thereby is the highly complex data that shows mixture distributions and censoring. For the main purpose, the extrapolation to other test conditions or designs, neither frequently used acceleration models like Arrhenius, nor complex models like Bayesian Mixtures-of-Experts or Bayesian networks give accurate lifetime predictions, although, the latter two are precise in case of interpolation. To compensate the limitations of ordinary regression models, we propose the application of a Gaussian process prior. The proposed model shows a high degree of flexibility by exploiting sums or products of appropriate covariance functions, e.g., linear or exponential, and serves as a reliable alternative to currently applied methods.

When estimating a performance measure f_* of a complex system from noisy data over a domain of interest, the underlying function f_* is often known to be convex. In this case, one often uses convexity to better estimate f_* by fitting a convex function to data. The traditional way of fitting a convex function to data, which is done by computing a convex function minimizing the sum of squares, takes too long to compute. It also runs into an "out of memory" issue for large-scale datasets. In this paper, we propose a computationally efficient way of fitting a convex function by computing the best fit minimizing the sum of absolute deviations. The proposed least absolute deviations estimator can be computed more efficiently via a linear program than the traditional least squares estimator. We illustrate the efficiency of the proposed estimator through several examples.

The use of Kriging surrogate models has become popular in approximating computation-intensive deterministic computer models. In this work, the effect of enhancing Kriging surrogate models with a (partial) set of gradients is investigated. While, intuitively, gradient information is useful to enhance prediction accuracy, another motivation behind this work is to see whether it is worth including the gradients versus their computation time. Test results of two analytical functions and a fluid-structure interaction (FSI) problem from bio-mechanics show that this approach, known as Gradient Enhanced Kriging (GEK), can significantly enhance the accuracy of Kriging models even when the gradient data is only partially available.

An important step in input modeling is the assessment of data being independently and identically distributed (IID). While this is straightforward when modeling stationary stochastic processes, it becomes more challenging when the stochastic process follows a non-stationary pattern where the probability distribution or its parameters depend on time. In this paper, we first discuss the challenges faced by using traditional approaches. We then introduce the Histograms and Rates for Input Analysis (HistoRIA) as a tool to facilitate input modeling. The tool automates the analysis process and significantly reduces the amount of time and effort required to test the IID assumptions. The generated HistoRIA plot is capable of effectively illustrating changes in the rate and distribution over time. Although originally designed and developed for simulation input analysis, the paper demonstrates how the tool can potentially be applicable in other areas where non-stationarity in the data is also common.

To provide automated access to multiple analysis tools, such as discrete event simulation or optimization, we extend current model-based systems engineering (MBSE) methodologies by introducing a new model to model transformation method based on object-oriented creational patterns from software design. Implemented in MATLAB’s discrete event simulation tool, SimEvents, we demonstrate the methodology by generating two distinct use cases based on a distribution supply chain and manufacturing system.

Goal-directed reproducible experimentation with simulation models is still a significant challenge. The underutilization of design of experiments, limited transparency in the collection and analysis of results, and ad-hoc adaptation of experiments as learning takes place continue to hamper reproducibility and hence cause a credibility gap. In this study, we propose a strategy that leverages the synergies between model-driven engineering, intelligent agent technology, and variability modeling to support the management of the lifecycle of a simulation experiment. Experiment design and workflow models are introduced for configurable experiment synthesis and execution. Feature-based variability modeling is used to design a family of experiments, which can be leveraged by ontology-driven software agents to configure, execute, and reproduce experiments. Online experiment adaptation is proposed as a strategy to facilitate dynamic experiment model updating as objectives shift from validation to variable screening, understanding, and optimization.

The life-cycle of a DES study goes through a number of phases, from initially goal setting to validation of experiments. Of these, the phases to support DES data collection and representation have been underrepresented in the literature to date. This paper sets out to describe a process of data collection and representation for DES within the context of the DES life-cycle. It is recognized that for large companies the data collection and representation phase differs when compared to SMEs. Due to the high complexity in performing a DES study in an SME data might not be in a DES ready format in existence whatsoever. This complexity can cost without budgets to meet it. This paper describes an expanded process in relation to the data collection and representation phase specifically for SMEs. Finally, a preliminary high level overview of a prototype is presented which supports this phase at an SME level.

A considerable amount of research on parallel discrete event simulation has been conducted over the past few decades. However, most of this research has targeted the parallel simulation infrastructure; focusing on data structures, algorithms, and synchronization methods for parallel simulation kernels. Unfortunately, distributed environments have high communication latencies that can reduce the potential performance of parallel simulations. Effective partitioning of the concurrent simulation objects of the real world models can have a large impact on the amount of network traffic necessary in the simulation, and consequently the overall performance. This paper presents our studies on profiling the characteristics of simulation models and using the collected data to perform partitioning of the models for concurrent execution. Our benchmarks show that Profile Guided Partitioning can result in dramatic performance gains in the parallel simulations. In some of the models, 5-fold improvements of the run time of the concurrently executed simulations were observed.

Microgrids (MGs) offer new technologies for semiautonomous grouping of alternative energy loads fed into a power grid in a coordinated manner. Simulations of these microgrids are time critical yet computationally demanding, inherently complex, and dynamic, especially when they are constructed for control purposes. In this paper, we address the design ranking and selection problem in MG simulations from a set of finite alternatives in the presence of stochastic constraints. Each design encapsulates a different level of control of the segregation mechanism within the system, and a performance function measured as a combination of the incurred cost and energy surety. Building on this performance function, optimal computing budget allocation (OCBA) method is used to efficiently allocate simulation replications for selecting the best design with significant accuracy and reasonable computational burden. Computational results on a multi-scale MG testbed have shown that OCBA algorithm outperforms equal and proportional to variance allocation of replications.

Systems biology studies the complex interactions of biological and biochemical systems rather than their individual molecular components. System biology simulations can be embarrassingly parallel jobs that have no dependency among individual simulation instances, and thus lend themselves to parallel execution over distributed resources to reduce their overall execution time. One example of such distributed resources is a Desktop Grid for Volunteer Computing that aims to use vast numbers of computers to support scientific applications. The SZTAKI Desktop Grid (SZDG) uses a modified form of the volunteer computing software BOINC to implement an institution-wide Desktop Grid. This paper reports on experiences of porting the SIMAP systems biology ODE simulator to SZDG. A case study using a simulation of the mammalian ErbB signaling pathway reports on significant speedup.

Modeling is a creative activity known and practiced by most in industry, government, and academia. A model is a construct of language coded using different technologies—from print and physical media to computer-generated synthetic environments. The activity of modeling is so central to human cognition, that we must ask ourselves whether modeling should be more clearly emphasized in education, especially within science, technology, engineering, and mathematics (STEM) fields. Coding has recently been suggested as a skill that everyone needs to acquire for the 21st Century. Can modeling be situated along side coding? We explore the connections between modeling and coding, and stress the importance of model literacy for all.

Due to the increasing availability of data and wider use of analytics, the ingredients for increased reliance on modeling and simulation are now present. Tremendous progress has been made in the field of modeling and simulation over the last six decades. Software and methodologies have advanced greatly. In the area of weather, future-casts based on model predictions have become highly accurate and heavily relied upon. This is happening in other domains, as well. In a similar vein, drivers may come to rely upon future-casts of traffic that are based on predictions from models fed by sensor data. The need for and the capabilities of simulation have never been greater. This panel will examine the future of research in modeling and simulation by (1) examining prior progress, (2) pointing out current weaknesses and limitations, (3) highlighting directions for future research, and (4) discussing support for research including funding opportunities.

Developing a simulation model needs lots of costs. If the model elements can be reused in newly developed models of the same physical system, the modeling costs will be reduced. Traditional DEVS model representations depend on programming languages. A modeler is difficult to identify the DEVS semantics of model elements, which limits the reuse of existing models. In this paper, the structured modeling technology is used to represent DEVS models. A DEVS model is represented as a structured model. An atomic model can be represented as a genus graph and a modular tree, and a coupled model can be represented as elemental detailed tables. Based on the visual representation, models can be stored, maintained and reused easily. Two cases for the application of structured DEVS model representation are also presented.

Discrete Event Simulation (DES) is traditionally one of the most popular operation research techniques. Nevertheless, organizations, and especially Small and Medium Enterprises (SMEs), are often reluctant to invest in DES projects. The high cost of developing and maintaining DES models, the high volume of data that these need in order to provide valid results and the difficulty in embedding them in real time problems, are some of the problems that deter organizations from adopting DES based solutions. DREAM is an FP7 project that has the aspiration to produce an Open Source (OS) platform, which will confront the above issues. The DREAM architecture consists of three cloud enabled modules, a semantic free Simulation Engine (SE), a Knowledge Extraction (KE) tool and a customizable web-based Graphical User Interface (GUI). We present how these components cooperate and how an advanced user can manipulate them in order to develop tailored solutions for companies.

Domain specific languages have been used in modeling and simulation as tools for model description. In recent years, the efforts toward enabling simulation reproducibility have motivated the use of domain specific languages also as the means with which to express experiment specifications. In simulation areas ranging from computational biology to computer networks, the emerging trend is to treat the experimentation process as a first class object. Domain specific languages serve to specify individual sub-tasks in this process, such as configuration, observation, analysis, and evaluation of experimental results. Additionally, they can be used in a broader scope, for instance, to describe formally the experiment's goals. The research and development of domain specific languages for experiment specification explores all of these and additional possible applications. In this paper, we discuss various existing approaches for defining this type of domain specific languages and present a critical analysis of our findings.

Microscopic traffic simulation is a well-accepted simulation approach for simulation problems where the effects of individual driver behavior and/or vehicle interactions need to be taken into account at a fairly detailed level. Such problems include the optimization of traffic light controls patterns or the design of lane layouts at intersections. Such simulation models typically require very detailed and accurate models of the underlying road networks. The manual creation of such networks constitutes a high effort, limiting the simulated area in practical applications to the absolutely necessary. With the increased availability of satellite based geographical data we investigate, if and how such data can be automatically transformed into route networks with adequate level of detail for microscopic traffic simulation models. We further outline the design of data structures for an extensible simulation framework for microscopic traffic simulation which is capable of including different types of publically available data sources.

Ciclovia or Open-Street Programs are free multi-sectorial programs for people from different socio-economic backgrounds where public spaces are closed to motorized traffic and open for leisure activities. Over the past few years the expansion rate of such programs worldwide has dramatically increased due to their general benefits to the public health and their resource-efficient implementation. Performance indicators of Ciclovia programs allow analyzing their impact on public health, with the number of participants being a key performance indicator. Thus the reliable estimation of this number is crucial to measuring the cost-effectiveness of the programs for the cities and municipalities, and a unified and flexible estimation methodology allows comparisons between programs. In this paper, we propose a model to estimate this indicator and apply our approach to a case study in the city of Bogota, with the largest program in the world, where we estimate an average of 675,000 participants per day.

M&S is a decision support technique that enables stakeholders to make better and more informed decisions; application of this to supply chains is referred to as supply chain simulation. The increasingly interconnected enterprise of the digital age benefit from cooperative decision making through the utilization of existing technological foundations, standards and tools (e.g., computer networks, data sharing standards, tools for collaborative working). Distributed Supply Chain Simulation (DSCS) facilitates such collective decision making by enabling simulation models of individual business processes/organizations to execute cooperatively over a computer network. The aim of this research is to identifying the advances in DSCS and its present state of play. Towards realization of this aim we present a methodological review of literature and complement this with our domain-specific knowledge in supply chains and parallel and distributed simulation.

We propose a data structure that stores previously observed vehicle paths in a given area in order to predict the forward trajectory of an observed vehicle at any stage. Incomplete vehicle trajectories are conditioned against in a Past Tree, to predict future trajectories in another tree structure - a Future Tree. Many use cases in transportation simulation benefit from higher validity by considering historical paths in determining how to route vehicle entities. Instead of assigning static and independent turn probabilities at intersections, the storage and retrieval of historical path information can give a more accurate picture of future traffic trends and enhance the capabilities of real-time simulations to, say, inform mobile phone users of expected traffic jams along certain segments, direct the search efforts of law enforcement personnel, or allow more effective synchronization of traffic signals.

In the context of a city-scale symbiotic traffic simulation, real-time data about the location of many vehicles are obtained in the form of a continuous data-stream. In this paper, we present a scalable solution for performing map-matching using sliding-windows over a GPS data-stream onto a digital road network for initializing the what-if analysis process involved in symbiotic simulations. We focus on the optimizations performed to ensure that the latency associated with the map-matching process is low while maintaining a high degree of accuracy. Experimental results reveal the range in terms of sampling interval and noise for acceptable reliability and latency.

The goal of this paper is to study the team formation of multiple UAVs and UGVs for collaborative surveillance and crowd control under uncertain scenarios (e.g., crowd splitting). A comprehensive and coherent dynamic data driven adaptive multi-scale simulation (DDDAMS) framework is adopted, with the focus on simulation-based planning and control strategies related to the surveillance problem considered in this paper. To enable the team formation of multiple UAVs and UGVs, a two stage approach involving 1) crowd clustering and 2) UAV/UGV team assignment is proposed during the system operations by considering the geometry of the crowd clusters and solving a multi-objective optimization problem. For the experiment, an integrated testbed has been developed based on agent-based hardware-in-the-loop simulation involving seamless communications among simulated and real vehicles. Preliminary results indicate the effectiveness and efficiency of the proposed approach for the team formation of multiple UAVs and UGVs.

AddSIM is a component-based simulation environment that has been developed for weapon system modeling and engagement simulation. While AddSIM addresses component-based model development and reuse issues, it does not fully consider distributed simulation for massive and frequently changing data. We studied some approaches to develop an engagement simulation environment based on data distribution service for distributed systems (DDS). This article introduces three different approaches to apply DDS to AddSIM and describes their advantages and disadvantages. Then, we choose the best way to develop the mixture of AddSIM and DDS. According to the proposed approach, we explain the data exchange mechanism between AddSIM nodes and time synchronization for correct execution. We also define several DDS topic types for interoperation. Finally, we describe an anti-ship warfare case study to show the difference between AddSIM and the proposed approach.

Interoperation of simulation models is an important issue due to high level requirements of reusability, scalability, and eventually training effect. Achieving Live, Virtual and Constructive (LVC) simulation interoperability is a main goal and a major challenge for M&S community. High interoperability quality in LVC simulation environment is a technologically complex task, being affected by multiple factors, and the task is not yet satisfactorily characterized and studied. This research presents an experimental LVC simulation framework to model and simulate complex war fighting scenarios. Our experimental framework implementation discusses key issues for LVC interoperability encountered during our experimentation. A case study is presented to discuss LVC integration and interoperability challenges. Our experimental research aim is to contribute to the definition and design concepts of LVC simulation systems developments, technological considerations and adequate interoperability.

AddSIM is a simulation environment for integrating models such as platforms, sensors, command and control systems, and shooters into the same synthetic battle field. AddSIM aimed to integrate the models which were developed and used during each weapon system development phase. AddSIM consists of four components; user interface including resource repositories, simulation engine (kernel), external interface, and support services. The user interface is a set of tools to make models, set-up scenarios, check execution options, run the kernel, and analyze the simulation results. The kernel can manage discrete-event and discrete-time hybrid simulations and run the simulation in stand-alone or distributed modes. It can also support parallel simulations. AddSIM can interoperate with legacy models in various forms, C/C++, MATLAB, HLA/RTI, and DIS, through the AddSIM external interface functions. The support services are environmental services (terrain, atmosphere, and maritime), spatial service, journaling/logging service, and utility service.

In May 2013, NASA requested a study to develop a discrete event simulation (DES) model that analyzes the launch campaign process of the Space Launch System (SLS) from an integrated commodities perspective. The scope of the study includes launch countdown and scrub turnaround and focuses on four core launch commodities: hydrogen, oxygen, nitrogen, and helium. Previously, the commodities were only analyzed individually and deterministically for their launch support capability, but this study was the first to integrate them to examine the impact of their interactions on a launch campaign as well as the effects of process variability on commodity availability. The model utilized the flow process modules in Rockwell Arena to simulate the commodity flows and calculate total use. The study produced a validated DES model that showed that Kennedy Space Center’s ground systems were capable of supporting a 48-hour scrub turnaround for the SLS.

Before the introduction of discrete rate simulation (DRS), simulating mixed continuous and discrete event (hybrid) systems presented unique challenges that existing simulation methodologies were not suited or adaptable enough to solve. In the 1990’s a new simulation methodology, discrete rate simulation, was developed to address those issues. The most challenging aspect of discrete rate simulation is to accurately calculate the movement of material (“flow”) over a wide range of simulated systems. To calculate flow rates, the first generation DRS technology used an iterative algorithm based on the propagation of potential rates. The iterative approach failed to provide an accurate calculation of flow for many DRS models, limiting the use of this technology to a small range of real world problems. Discussed here is how the second generation DRS technology uses a global oversight approach to model the full range of DRS systems and resolve the issues associated with earlier methods.

Demographic microsimulation often focuses on effects of stable macro constraints on isolated individual life course decisions rather than on effects of inter-individual interaction or macro-micro links. To change this, modeling and simulation have to face various challenges. A modeling language is required allowing a compact, succinct, and declarative description of demographic multi-level models. To clarify how such a modeling language could look like and to reveal essential features, an existing demographic multi-level model, i.e., the linked life model, will be realized in three different modeling approaches, i.e., ML-DEVS, ML-Rules, and attributed pi. The pros and cons of these approaches will be discussed and further requirements for the envisioned language identified. Not only for modeling but also for experimenting languages can play an important role in facilitating the specification, generation, and reproduction of experiments, which will be illuminated by defining experiments in the experiment specification language SESSL.

Using 3D laser scanning, the spatial data of an entire production system can be captured and digitalized in a matter of hours. Such spatial data could provide a current state representation of the real system available at the hand of the simulation engineer. The purpose of this paper is to evaluate the use of 3D laser scanning in Discrete Event Simulation (DES) projects in the area of production systems. The evaluation relies on three simulation studies performed with the support of 3D laser scanning. 3D scan data, if available, can support most steps in a DES study. Particularly, the 3D scan data acts as a reference model when formulating the conceptual model and collecting input data. During model building the scan data provides physical measurements for accurate positioning of simulation objects. Furthermore the scan data can be used for photorealistic visualization of the simulated environment without requiring any CAD modeling.

This paper introduces a probabilistic algorithm for solving the well-known Facility Location Problem (FLP), an optimization problem frequently encountered in practical applications in fields such as Logistics or Telecommunications. Our algorithm is based on the combination of biased random sampling -using a skewed probability distribution– with a metaheuristic framework. The use of random variates from a skewed distribution allows to guide the local search process inside the metaheuristic framework which, being a stochastic procedure, is likely to produce slightly different results each time it is run. Our approach is validated against some classical benchmarks from the FLP literature and it is also used to analyze the deployment of service replicas in a realistic Internet-distributed system.

The use of process modeling combined with the use of simulation-based analysis provides a valuable way to analyze business processes (BPs) and to evaluate design alternatives before committing resources and effort. The simulation-based analysis of BPs usually addresses performance in terms of efficiency, i.e., focusing on time-related properties (e.g., throughput or execution time). Differently, this paper proposes an automated method for the analysis of BPs in terms of both efficiency-related performance and reliability. In addition, the method allows business analysts to carry out a joint performance and reliability analysis by introducing a so-called performability attribute. The proposed method is illustrated by use of a running example dealing with a conventional e-commerce scenario.

A solution methodology and implementation components are presented that can uncover unwanted, unintentional or unanticipated effects on electric grids from changes to actual electric grid control software. A new design is presented to leapfrog over the limitations of current modeling and testing techniques for cyber technologies in electric grids. We design a fully virtualized approach in which actual, unmodified operational software under test is enabled to interact with simulated surrogates of electric grids. It enables the software to influence the (simulated) grid operation and vice versa in a controlled, high fidelity environment. Challenges in achieving such capability include achieving low-overhead time control mechanisms in hypervisor schedulers, network capture and time-stamping, translation of network packets emanating from grid software into discrete events of virtual grid models, translation back from virtual sensors/actuators into data packets to control software, and transplanting the entire system onto an accurately and efficiently maintained virtual-time plane.

Computer exploits often involve an attacker being able to compromise a sequence of hosts, creating a chain of "stepping stones" from his source to ultimate target. Stepping stones are usually necessary to access well-protected resources, and also serve to mask the attacker's location. This paper describes means of constructing models of networks and the access control mechanisms they employ to approach the problem of finding which stepping stone paths are easiest for an attacker to find, and the computational complexity of the problem as a function of knowledge we may have about limitations on attackers within a compromised host. While the simplest formulation of the problem can be addressed with deterministic shortest-path algorithms, we argue that consideration of what and how an attacker may (or may not) launch from a compromised host pushes one towards simulation based solutions.

The ubiquity of smart phones and mobile devices has led to new security challenges in the form of mobile malware. Understanding its spread requires a high performance computing approach that involves: (i) a realistic and detailed representation of mobile social networks in urban environments, and (ii) a scalable simulation environment for malware diffusion.

We use Epicure (Channakeshava et al., IPDPS 2011), an individual based HPC simulation tool for malware modeling, that scales to networks with over 10M individuals. We compare malware dynamics in realistic networks with random mobility models and study the impact of key properties associated with the worm and mobility models. We use detailed statistical analysis to identify the significant parameters and their interaction. Finally, we use Epicure to study SMS/MMS based malware and hybrid malware that use both proximity based Bluetooth networks and infrastructure based cellular network to replicate on other susceptible devices.

Modeling large scale Web search engines (WSEs) is a complex task. It involves many issues such as representing user's behavior, query traffic, several strategies and heuristics to improve query response time, etc.. Typically, WSEs are composed of several services deployed in data centers, which must interact to get the best document results to user queries. Additionally, hardware specification like multithreading and network communications have to be taken into account. In this paper, we propose to model a service-based WSE using the Discrete Event System Specification (DEVS) formalism, which is one of the most powerful methodologies for discrete event systems. We validate our proposed model against an actual MPI implementation of the WSE and a process oriented simulation. We evaluate the accuracy of the proposed model by evaluating metrics such as query throughput and we show that there is no relevant differences, just small fluctuations less than 4%.

The fat tree topology with multipath capability has been used in many recent data center networks (DCNs) for increased bandwidth and fault tolerance. Traditional routing protocols have only limited support for multipath routing, and cannot fully utilize the available bandwidth in such networks. In this paper, we study multipath routing for fat tree networks. We formulate the problem as a linear program and prove its NP-completeness. We propose a practical solution, which takes advantage of the emerging software-defined networking paradigm. Our algorithm relies on a central controller to collect necessary network state information in order to make optimized routing decisions. We implemented the algorithm as an OpenFlow controller module and validated it with Mininet emulation. We also developed a fluid-based DCN simulator and conducted experiments, which show that our algorithm outperforms the traditional multipath algorithm based on random assignments, both in terms of increased throughput and in reduced end-to-end delay.

Agent-based modeling (ABM) approach is used to reassess the Barabasi-Albert (BA) model, the classical algorithm used to describe the emergent mechanism of scale-free networks. This approach allows for the incorporation of agent heterogeneity which is rarely considered in BA model and its extended models. The authors argue that, in social networks, people's intention to connect is not only affected by popularity, but also strongly affected by the extent of similarity. The authors propose that in forming social networks, agents are constantly balancing between instrumental and intrinsic preferences. The proposed model allows for varying the weighting of instrumental and intrinsic preferences on the agents attachment choices. The authors also find that changing preferences of individuals can lead to significant deviations from power-law degree distribution. Given the importance of intrinsic consideration in social networking, the findings emerged from this study is conducive to future studies of social networks.

Distributed simulations provide a powerful framework for utilizing a greater amount of computing resources, but require that consideration be taken to ensure that the results of these simulations match results that would have been produced by a single sequential resource. Also, synchronization among the multiple nodes must also occur to ensure that events processed in the simulation maintain a timestamped order across all nodes. This work uses the popular network simulator ns-3. The ns-3 simulator is a discrete event network simulator used for educational and research-oriented purposes which provides two implementation options for distributed simulations based on the null message and granted time window algorithms. We examine the performance of both distributed schedulers available in ns-3 in an effort to gauge specific features of an overall distributed network topology that warrant the use of one synchronization method over the other.

MPI collective operations are a critical and frequently used part of most MPI-based large-scale scientific applications. In previous work, we have enabled Rensselaer Optimistic Simulation System (ROSS) to predict the performance of MPI point-to-point messaging on high-fidelity million-node network simulations of torus and dragonfly interconnects. The main contribution of this work is an extension of these torus and dragonfly network models to support MPI collective communication operations using the optimistic event scheduling capability of ROSS. We demonstrate that both small- and large-scale ROSS collective communication models can execute efficiency on massively parallel architectures. We validate the results of our collective communication model against the measurements from IBM Blue Gene/Q and Cray XC30 platforms using a data-driven approach on our network simulations. We also perform experiments to explore the impact of tree degree on the performance of collective communication operations in large-scale network models.

As many other kinds of simulation experiments, simulations of computer networks tend to generate high volumes of output data. While the collection and the statistical processing of these data are challenges in and of themselves, creating meaningful visualizations from them is as much an art as it is a science. A sophisticated body of knowledge in information design and data visualization has been developed and continues to evolve. However, many of the visualizations created by the network simulation community tend to be less than optimal at creating compelling, informative narratives from experimental output data. The primary contribution of this paper is to explore some of the design dimensions in visualization and some advances in the field that are applicable to network simulation. We also discuss developments in the creation of the visualization subsystem in the Simulation Automation Framework for Experiments (SAFE) in the context of best practices for data visualization.

Inter-cell interference (ICI) is considered as the most critical bottleneck to ubiquitous 4th generation cellular access in the mobile long term evolution (LTE). To address the problem, several solutions are under evaluation as part of LTE-Advanced (LTE-A), the most promising one being coordinated multi-point joint transmission (CoMP JT). Field tests are generally considered impractical and costly for CoMP JT, therefore the need to provide a comprehensive and high-fidelity computer model to understand the impact of different design attributes and the applicability use cases. This paper presents a novel approach to the modelling and simulation of an LTE-A system with CoMP JT by means of discrete event simulation (DES) using OPNET modeler. Simulation results are provided for a full buffer traffic model and varying the characteristics of the interface between cooperating points. Gains of up to 120% are achieved for the system throughput when using CoMP JT.

In this work, we applied modeling and simulation to plan and evaluate the capacity of a real enterprise voice gateway system. We modeled the analyzed voice gateway and assessed it under different workload scenarios. We evaluated the actual setup under the real current workload demand, as well as future expected demands in terms of voice long-distance calls using PSTN and VoIP providers. Also, the existing voice gateway capacity was tested facing calls to mobile phones through GSM Sim cards. Finally, we tested a proposal setup to reduce the number of simultaneous E1 channels used per voice call, and found that our approach reduced the E1 usage rate by 14%.

Vehicular networks are envisioned to play an important role in the building of intelligent transportation systems. However, the dangers of the wireless transmission of potentially exploitable information such as detailed locations are often overlooked or only inadequately addressed in field operational tests or standardization efforts. One of the main reasons for this is that the concept of privacy is difficult to quantify. While vehicular network algorithms are usually evaluated by means of simulation, it is a non-trivial task to assess the performance of a privacy protection mechanism.

In this paper we discuss the principles, challenges, and necessary steps in terms of privacy assessment in vehicular networks. We identify useful and practical metrics that allow the comparison and evaluation of privacy protection algorithms. We present a systematic literature review that sheds light on the current state of the art and give recommendations for future research directions in the field.

Increased focus on energy cost savings and carbon footprint reduction efforts improved the visibility of building energy simulation, which became a mandatory requirement of several building rating systems. Despite developments in building energy simulation algorithms and user interfaces, there are some major challenges associated with building energy simulation; an important one is the computational demands and processing time. In this paper, we analyze the opportunities and challenges associated with this topic while executing a set of 275 parametric energy models simultaneously in EnergyPlus using a High Performance Computing (HPC) cluster. Successful parallel computing implementation of building energy simulations will not only improve the time necessary to get the results and enable scenario development for different design considerations, but also might enable Dynamic-Building Information Modeling (BIM) integration and near real-time decision-making. This paper concludes with the discussions on future directions and opportunities associated with building energy modeling simulations.

Net-zero buildings emphasize balance between the consumption and production of resources, resulting in structures that are not only more efficient, but potentially restorative. While historically applied to energy use, the net-zero framework is also relevant to water management. Both the building energy and water sectors consist of demand loads that must be met by available sources. Variations in load design, source allocation, and human interaction result in numerous arrangements that require evaluation to meet efficiency goals. Decision support systems aimed at building energy are abundant, whereas building water tools are limited. The dynamic nature of the building water cycle necessitates flexible modeling tools that can predict and assess future water consumption and production trends at varying resolutions and under fluctuating conditions. This paper presents opportunities for simulation modeling to support net-zero water achievement and introduces an integrated building water management (IBWM) model for on-site water balance decision support.

A case of study applied to the sports context (Gremio Arena Soccer in Brazil), this paper has the objective to measure dynamically the time spent during the accommodation of fans in a stadium bleacher. The metric used to evaluate it was the occupation time. For this analysis, it was considered four different occupation scenarios of the bleachers characterizing ways to drive fans to their seats. The selected scenarios were: (1) random entry, (2) entry driven by a dispatchers, (3) numbered seats; (4) numbered seats with dispatcher. To built the model it was used the software SIMIO, capable to tackle this type of experiment. With a conclusive recommendation it was observed that the policy based on the dispatcher results in a shorter total occupation time. Additionally, it was analyzed factors that should make difference, such as physical feasibility to support the large number of people, as well as cultural aspects.

Commercial buildings consume nearly 20% of all energy used in the USA, costing over $200 billion each year. Building envelopes plays a key role in determining how much energy is required for the operation of a building. Thermal and solar properties of glazing and shading systems only provide information based on static evaluations, but it is critical to assess the efficiency of these systems as a whole assembly under site specific conditions. With an ever increasing cooling energy demand of buildings in hot and humid climates like in Florida, using a well-designed window-shading system is considered an efficient strategy that minimizes direct sunlight reaching indoors and thus reduces the overall energy loads. While energy loads reduction is important, the indoor comfort of occupants cannot be compromised. This research was conducted to analyze the indoor thermal and visual performance of various window-shading assemblies that were selected after their energy performance evaluation.

Adjusting for occupancy, when controlling an HVAC (Heating, Ventilation, and Air Conditioning) system, is an important way to realize demand-driven control and improve energy efficiency in buildings. Energy simulation is an efficient way to examine the effects of occupancy on a building’s energy consumption and a cost-effective and non-intrusive solution to test occupancy-based HVAC control strategies. However, there are more than one hundred building energy simulation programs used in research and practice, and large discrepancies exist in simulated results when different simulation programs are used to model the same building under same conditions. This paper evaluates different methods and sequences of coupling occupancy information with building HVAC energy simulation. A systematic review is conducted to analyze five energy simulation programs, including DOE-2, EnergyPlus, IES-VE, ESP-r, and TRNSYS, from the following five perspectives of heat transfer and balance, load calculation, occupancy-HVAC system connection, HVAC system modeling, and HVAC system simulation process.

Energy efficient building design demands a complete understanding of building envelope heat transfer along with airflow behavior. Although existing building energy modeling tools provide 2D heat transfer analysis, they fail to execute full-scale 3D heat transfer analysis and lack proper integration with Building Information Modeling BIM tools. This paper addresses these issues first by developing a BIM-integrated plugin tool to extract building geometry and material information from a 3D building model and then demonstrating a complete 3D heat transfer analysis along with grid generation. This paper discusses the preliminary research work in data extraction from Building Information Modeling (BIM) for performing 3D heat transfer in a seamless manner. This approach will help towards the implementation of a 3D heat transfer in Dynamic-BIM Workbench, an integrative, collaborative, and extensible environment. This Workbench enables integration of domain modeling, simulation, and visualization.

The purpose of this research is to provide a lifecycle building sustainability evaluation method to guide different stakeholders in how to apply sustainable practices and maintain the expected sustainability. Building Information Modeling (BIM) is selected to be a platform to integrate all the information to improve interoperability. Green standards are embedded in the BIM model and a rule-based system is developed to automatically evaluate the design and the building performance. Data are collected by using a Real-Time Location System (RTLS) and are used to update the BIM model. The as-built model is checked to see if it matches the sustainability aspects regarding the construction processes. During operation, energy consumption data are collected and analyzed. The performance of the building is checked to see if the designed features reach the sustainability goals. By integrating the BIM, RTLS, and other information, a prototype system of lifecycle sustainability evaluation is developed and tested.

Net zero energy is a topic that is trending in the construction industry. A part of the net zero movement garnering the most attention is K-12 public school construction. Alachua County’s Meadowbrook Elementary School (K-5) is a high performance school which can achieve net zero energy status with some proven and effective practices. In this paper, we discuss and compare the current baseline energy usage of the school since its completion and target opportunities to reduce energy usage. Recommendations based on the ASHRAE Advanced Energy Design Guide (50% Energy Savings) with the help of energy modelling and simulation would close the gap needed to make Florida schools energy self-sufficient. Further renewable energy production will be added by taking advantage of the Florida climate zone. The suggestions reviewed and applied in this paper will establish guidelines for prospective net zero energy schools in general and the Florida based schools in particular.

This paper describes the implementation of an indoor positioning architecture based on radio frequency profiling using received signal strength (RSS) measurements for localizing and tracking resources in construction-related applications. The profiling-based approach is coupled with commonly used noise filtering algorithms in order to cope with the application of material tracking in a pipe spool fabrication shop. With 95% likelihood, consistent positioning accuracy of 1-2 meters away from the actual position of a tracked tag can be obtained in the fabrication shop−which is deemed sufficient for materials and labor hours tracking in support of shop production control. In particular, through simulation experiments using data collected from a pipe fabrication shop we investigated the sensitivity of the resulting localization accuracy with respect to the quantity and layout of the reference points, aimed at streamlining system updating and simplifying solution implementation.

Conventionally, efforts are made to optimize the performance of simulation models by examining several possible resource combinations. However, the number of possible resource assignments increases exponentially with the increase of the range of available resources. Many researchers combined Genetic Algorithms (GAs) and other optimization techniques with simulation models to reach the Pareto solutions. However, due to the large number of resources required in complex and large-scale construction projects, which results in a very large search space, and the lack of the GA capability in fast convergence to the optimum results, parallel computing is required to reduce the computational time. This paper proposes the usage of Non-dominated Sorting Genetic Algorithm (NSGA-) as the optimization engine integrated with Discrete Event Simulation (DES) to model the bridge construction processes. The parallel computing platform is applied to reduce the computation time necessary to deal with multiple objective functions and the large search space.

This paper proposes and tests an integrated framework for bottom-up simulation of performance in construction projects. The proposed framework conceptualizes construction projects as systems-of-systems in which the abstraction and micro-simulation of dynamic behaviors are investigated at the base-level consisting of the following elements: human agents, information, and resources. The application of the proposed framework is demonstrated in a numerical example related to a tunneling project. The findings highlight the capability of the proposed framework in providing an integrated approach for bottom-up simulation of performance in construction projects.

Despite recent advancements, the time, skill, and monetary investment necessary for hardware setup and calibration are still major prohibitive factors in field data sensing. The presented research is an effort to alleviate this problem by exploring whether built-in mobile sensors such as global positioning system (GPS), accelerometer, and gyroscope can be used as ubiquitous data collection and transmission nodes to extract activity durations for construction simulation input modeling. Collected sensory data are classified using machine learning algorithms for detecting various construction equipment actions. The ability of the designed methodology in correctly detecting and classifying equipment actions was validated using sensory data collected from a front-end loader. Ultimately, the developed algorithms can supplement conventional simulation input modeling by providing knowledge such as activity durations and precedence, and site layout. The resulting data-driven simulations will be more reliable and can improve the quality and timeliness of operational decisions.

Geographical Simulation Model (GSM) is an alternative decision support system to assess transportation, agglomeration, as well as economic growth behavior according to logistics infrastructure improvement and development. This simulation model is based on spatial economics and includes seven economic sectors, including manufacturing and non- manufacturing. Traditional impact assessment methods like Computable General Equilibrium (CGE) model are often subject to limitations when providing analyses at the sub-national level. The available routes of highways, railways, and sea and air shipment are incorporated in the GSM model. The transaction cost within regions is modelled as determined by firms’choice to choose the course with the lowest trade costs. It also includes the estimates of some border cost measures such as tariff rates, non-tariff barriers, other border costs, etc. Enhancing ASEAN (the Association of Southeast Asian Nations) Economic Community by improving its infrastructure is used to demonstrate the GSM framework as a decision support system.

The objective of this paper is to propose and test a framework for integrated assessment of infrastructure systems at the interface between the dynamic behaviors of assets, agencies, and users. A hybrid agent-based/mathematical simulation model is created and tested using a numerical example related to a roadway network. The simulation model is then used in investigation of multiple performance scenarios pertaining to the road assets at the network level. The results include the simulation and visualization of the impacts of budget constraints on performance of the network over a forty-year policy horizon. The results highlight the significance of assessment of the interactions between infrastructure assets, agencies, and users and demonstrate the capabilities of the proposed modeling framework in capturing the dynamic behaviors and uncertainties pertaining to civil infrastructure management.

An essential part of the planning and control of any manufacturing system is the development of a model of the key processes. The Critical Path Method (CPM) is the most widely used modeling method in construction due to its simplicity. Discrete-event simulation is more versatile than CPM and is well suited to modeling manufacturing processes since these tend to be repetitive, but it lacks the simplicity in use of CPM and thus has not been widely adopted in construction. This paper demonstrates an alternative modelling approach, Foresight, developed to provide the modeling versatility of simulation, and yet be relatively simple to use and visually insightful. Previous work demonstrated the application of Foresight to in-place construction work and compared its performance to conventional simulation. This paper extends this work, demonstrating the application of Foresight to manufactured construction processes whereby streams of jobs, with design variances, are executed within a factory.

In an effort to explore the complexity of construction organizations, this paper introduces a comprehensive Agent Based Model called Virtual Organizational Imitation for Construction Enterprises (VOICE). Building its ground on the findings of organizational behavior, VOICE models three critical aspects of construction organizations including Work, Actors and Organization. Then different levels of organization processes are reproduced to simulate the transition from micro-level processes to the collective performance. As an attempt of developing a comprehensive, all-inclusive simulation model for construction organizations, this work sets a stepping-stone for future studies.

The critical path method (CPM) provides the standard approach to scheduling construction projects. Limited crew resources compound CPM analysis by imposing resource availability constraints. However, there is no generalized methodology yet to quantitatively determine the optimal quantities of resources to execute specific work packages based on CPM analysis. Furthermore, in project evaluation and review technique (PERT) simulation, the occurrence of uncertain events is represented by probability distributions for activity durations in an implicit fashion. In this paper, a bi-level project simulation methodology is proposed to (1) quantitatively determine the optimal resource quantities and activity times for each work package and (2) estimate total project duration and man-hours budget at a high level for project planning through Monte Carlo simulation, based on defining a limited quantity of likely scenarios for each work package. An industrial plant shutdown and turnaround project serves as case study to illustrate application of the proposed methodology.

Established techniques like the Critical Path Method and Linear Scheduling Method are activity centered and exhibit schedules statically, which impedes their ability to plan and control projects holistically. Scheduling therefore should be enhanced by incorporating new capabilities of measuring and displaying the dynamic nature of projects. In another technical field that employs a time-space coordinate system, however, traffic engineering, researchers successfully apply various parameters to measure the performance of an inherently dynamic behavior, which is identified as having significant potential to be adapted for scheduling purposes. This paper identifies concepts in traffic measurement that currently lack analogies in scheduling, including signals and trajectories. They are modeled with singularity functions, range-based expressions for variable phenomena, for new application in linear scheduling. Examples demonstrate the feasibility of deriving analogies from a related engineering field, which provides a com-pass to navigate future research to explore concepts that emerge from interaction of dynamic elements.

A simulation-based Earned Value Management methodology is introduced for modeling and analyzing project networks with stochastic activity times and activity costs. The methodology uses simulation to establish a project plan and estimate the planned value over the life of the project. During project execution, these reference measures can be used to determine if the project is on track to completion. The simulation tool can be used to evaluate potential alternatives and predict their impact on the project.

Layout planning for construction projects comprises two tasks: facility layout planning (FLP) and material layout planning (MLP), which has significant impacts on project cost and time. FLP specifies where to position temporary facilities on the site, and MLP determines the position of the material in the storage yard. This study focuses on MLP and describes a simulation-based method to improve material yard layout. In this method, simulation is employed for modeling the material handling process to evaluate material handling time. Due to the broad domain of possible solutions, simulation is integrated with genetic algorithm to heuristically search for a near optimum material layout with the least haulage time. The implementation of the proposed method is demonstrated in a case study which shows the superiority of the developed method over conventional methods. This paper also discusses how the results of this research can contribute to FLP.

In a steel fabrication shop, jobs from different clients and projects are generally processed simultaneously in order to streamline processes, improve resource utilization, and achieve cost-effectiveness in serving multiple concurrent steel-erection sites. Reliable quantity takeoff on each job and accurate estimate of shop fabrication man-hour requirements are crucial to plan and control fabrication operations and resource allocation on the shop floor. Building information modeling (BIM), is intended to integrate multifaceted characteristics of a building facility, but finds its application in structural steel fabrication largely limited to design and drafting. This research focuses on extending BIM’s usage further to the planning and control phases in steel fabrication. Using data extracted from BIM-based models, a linear regression model is developed to provide the man-hour requirement estimate for a particular job. Actual data collected from a steel fabrication company was used to train and validate the model.

A simulation-based heuristic is presented for a resource investment problem (RIP). This version of the RIP considers the trade-off between the number of resources, project makespan and resource utilization. A “win-win” goal is a reduction in project makespan while improving resource utilization. The RIP heuristic is presented as an executive for RCAN, a simulation tool that produces solutions to the multi-mode resource constrained project scheduling problem (RCPSP). The RIP heuristic uses feedback from RCAN to iteratively modify a set of renewable resources. The heuristic is shown to be effective on real-world, large-scale depot maintenance projects. In addition, the simulation tool uses a priority rule approach to schedule project tasks for the RCPSP problem. Therefore the RCPSP priority rule has a major effect on the RIP heuristic. An analysis is presented showing how various priority rules impact the RIP heuristic’s ability to reduce the makespan while maintaining or increasing resource utilization.

Customized planning, engineering and build-up of factory plants are very complex tasks, where project management contains lots of risks and uncertainties. Existing simulation techniques could help massively to evaluate these uncertainties and achieve improved and at least more robust plans during project management, but are typically not applied in industry, especially at SMEs (small and medium-sized enterprises). This paper presents some results of the joint research project simject of the Universities of Paderborn and Kassel, which aims at the development of a demonstrator for a simulation-based and logistic-integrated project planning and scheduling. Based on the researched state-of-the-art, requirements and a planning process are derived and described, as well as a draft of the current technical infrastructure of the intended modular prototype. First plug-ins for project simulation and multi-project optimization are implemented and already show possible benefits for the project management process.

Self-adaptive discrete-event simulation is a general paradigm for time integration of discretized partial differential equations and particle models. This novel approach enables local time steps for equations describing time evolution of grid-based elements (fluids, fields) and macro-particles on arbitrary grids while preserving underlying conservation laws. The solution-adaptive integration ensures robustness (stability) and efficiency (speed) of complex nonlinear simulations. Using this technique we achieved a breakthrough in simulations of multiscale plasma systems. A new particle-in-cell simulation tool, HYPERS (Hybrid Particle Event-Resolved Simulator), which solves a set of strongly coupled Maxwell’s equations, electron fluid equations and ion particle equations of motion, is presented as the first multi-dimensional application of this technology. We discuss its parallel implementation and demonstrate first results from three-dimensional simulations of compact plasma objects that have been out of reach of conventional codes. Potential applications of the new methodology to other scientific and engineering domains are also discussed.

We describe, in this paper, our progress in developing a simulation environment to be used for the detailed simulation of chemical reactions and the diffusion of ions through a neuronal membrane. Neuron Time Warp (NTW) is part of the Neuron project (www.neuron.yale.edu) and is intended to be made use of by experimental neuroscientists. It relies upon the Next Subvolume Method, a stochastic Monte Carlo algorithm used to simulate chemical reactions within the membrane of a neuron. We make use of a model of a dendrite branch on which to evaluate NTW's performance. We evaluated the performance of NTW using MPI and shared memory models on a multi-core machine as a first step towards the development of a combined differential equation/ discrete event model of a neuron.

Virtual worlds are general-purpose real-time simulation of three-dimensional environments, and serve for several purposes, such as physics simulation, collaboration, and entertainment. Due to the real-time nature of these simulations, scaling the number of in-world entities and interacting users is challenging. In this paper we present a novel approach to scalable virtual worlds, combining two dimensions of workload partitioning: space and operations. We present this new design as the Distributed Scene Graph with microcells (DSG-M), and evaluate our approach in a distributed physics intensive evaluation aimed at testing two hypothesis: (1) the space partitioning approach improves scalability by balancing the load of an overwhelmed physics dedicated simulator; and (2) simulation precision can be maintained by assigning read-only spaces near the partition borders. Results show evidence to confirm both hypotheses, and of successfully scaling the simulation of an overwhelming workload.

We present a GPU-based simulator engine that performs all steps of large-scale network simulations on a commodity many-core GPU. Overhead is reduced by avoiding unnecessary data transfers between graphics memory and main memory. On the example of a widely deployed peer-to-peer network, we analyze the parallelism in large-scale application-layer networks, which suggests the use of thousands of concurrent processor cores for simulation. The proposed simulator employs the vast number of parallel cores in modern GPUs to exploit the identified parallelism and enables substantial simulation speedup. The simulator adapts its configuration at runtime in order to balance parallelism and overheads to achieve high performance for a given network model and scenario. A performance evaluation for simulations of networks comprising up to one million peers demonstrates a speedup of up to 19.5 compared with an efficient sequential implementation and shows the effectiveness of the runtime adaptation to different network conditions.

One of the issues of parallelizing large-scale agent-based traffic simulations is partitioning and load-balancing. Traffic simulations are dynamic applications where the distribution of workload in the spatial domain constantly changes. Dynamic load-balancing at run-time has shown better efficiency than static partitioning in many studies. However, existing work has only focused on geographic partitioning methods which do not consider the minimization of communication overhead. In this paper, a graph-based dynamic load-balancing mechanism which minimizes the communication overhead during load-balancing operations is developed. Its efficiency is investigated in the agent-based traffic simulator SEMSim Traffic using real world traffic data. Experiment results show that it has significantly better performance than static graph partitioning methods in improving the overall speed of the simulation.

Real Time Strategy (RTS) games provide complex domain to test the latest artificial intelligence (AI) research. In much of the literature, AI systems have been limited to playing one game. Although, this specialization has resulted in stronger AI gaming systems it does not address the key concerns of AI research, which focuses on the development of AI agents that can autonomously interpret, learn, and apply new knowledge. To achieve human level performance, current AI systems rely on game specific knowledge of an expert. This paper proposes a RTS language in hopes of shifting the current research focus to the development of general RTS agents. General RTS agents are AI gaming systems that can play any RTS game, defined in the RTS language. The structure of the RTS language prevents game specific knowledge from being hard coded into the system, thereby facilitating research that addresses the fundamental concerns of artificial intelligence.

Gaming simulation allows for experiments with sociotechnical systems and has as such been employed in the railway sector to study the effects of innovations on robustness and punctuality. Systems work as non-trivial machines and the effect of an innovation on a dependent variable is potentially context, time and history dependent. However, several constraints inhibit the use of validity increasing measures such as repeated runs and increasing sample size. Based on a debriefing framework, insights from qualitative process research and six games with Dutch and UK railway traffic operators, we provide a guide on how to assess and increase reliability and validity. The key is for game players, observers and facilitators to open up the black box and thereby assessing how the innovation brought about any changes, if these changes are insensitive to changes in parameters and if the conclusions hold outside the game.

Management games have a long history in management and social science education, and a large number of such games has been developed and is being used in university education and in professional training. With the increasing use of computers in recent decades, most of them have been developed in computerized form. However, typically, these games are being developed in isolation, without using any general model, or methodology, or simulation engineering framework. In this paper we propose a basic conceptual model for business management games based on the classical Lemonade Stand Game and we show how to construct incremental extensions of this model and how to implement them as web-based simulations using standard web technologies.

The idea of the magic circle, frequently referenced in the serious gaming community, is explained and then shown to be a possible perspective to view live-virtual-constructive (LVC) simulation federations. This view opens the method of categorizing different roles and data capabilities for the various elements and actors within an LVC federation. The applicability of this view is shown in some explanatory detail, including a description of its applicability to simulations, the different types of roles, and the variety of knowledge (procedural and propositional) that can be qualified when the federation is viewed this way. Structured identification of the federation elements and the data they are exchanging is relied on to describe peculiar data interoperability issues that exist within current LVC federation architectures, and which may also affect future LVC federation architectures currently under development.

A Distributed Virtual Environment (DVE) system provides a shared virtual environment where physically separated users can interact and collaborate over a computer network. There are three major challenges to improve DVE scalability: effective DVE system performance measurement, understanding the controlling factors of system performance/quality and determining the consequences of DVE system changes. We describe a DVE Scalability Engineering (DSE) process that addresses these three major challenges for DVE design. The DSE process allows us to identify, evaluate, and leverage trade-offs among DVE resources, the DVE software, and the virtual environment. We integrate our load simulation and modeling method into a single process to explore the effects of changes in DVE resources.

This paper discusses the implementation of the Trading Competition held at the 2014 IEEE Computational Intelligence in Financial Engineering conference (CIFEr 2014). Participants in the competition were asked to hedge a simulated portfolio of assets, worth approximately 54 million. The winner was the individual whose portfolio most closely generated a 1% annualized return based on daily tracking. The goal of the competition was to provide participants with the opportunity to learn portfolio management and hedging skill. Self-assessments indicate that contestants improved their portfolio management skills and enjoyed their experience. This paper discusses methods used to generate the simulated stock and option prices and to construct the trading platform. All of the software used in the competition is being made open source in the hope that students, professors, and practitioners improve on the idea of the competition, thereby facilitating project-based learning for the future practitioners of economics, finance, and financial engineering.

Analyzing systems can be a complex task especially when there is feedback across several variables in the model. Formal mathematical notation makes it difficult to understand the influences of feedback and cause/effect. Forrester created the System Dynamics methodology as a means to assist in this understanding by employing a hydraulic analogy. In this methodology, variables become simulated objects such as water valves or tanks. A variety of implementations allow users to construct and simulate these models. The problem is that for many implementations, the intuitive nature of water flow, intended by the methodology, is not as clear as it could be. For instance, the rate of flow or level in a tank may not be visualized. For novices, we suggest that this issue, as well the ability to understand relationships and linking across multiple representations can be problematic. We designed and describe a web-based interface that solves these problems.

Dynamic computer simulations seek to engage the viewer by providing an intuitive representational mapping of common knowledge features to new knowledge concepts. Our research aims to provide enhanced understanding of complex systems through participatory interaction with our dynamic simulation models. Previous research has indicated that virtual and tangible models are well suited for use in informal education spaces, as they increase user interaction and curiosity amongst children and adults. We designed and implemented an interactive virtual environment as well as an interactive tangible “water computer” to represent the complex interspecies behavior of Lotka-Volterra predator-prey dynamic system. We designed our simulation models for use in informal STEM education settings, with a design focus on enhanced interactions and reflexive thinking.

The Intelligence, Surveillance, and Reconnaissance (ISR) domain offers a rich application environment for Soldier-Robot teaming and involves multiple tasks that can be effectively allocated across human and robot assets based upon their capabilities. The U.S. Armed Forces envisions Robot-Aided ISR (RAISR) as a strategic advantage and decisive force multiplier. Given the rapid advancement of robotics, Human Systems Integration (HSI) represents a critical risk to the success of RAISR. Simulation-Based Training (SBT) will play a key role in mitigating HSI risks and migrating from traditional Soldier-Robot operation to mixed-initiative teaming. However, research is required to understand the SBT methods and tools most applicable to the RAISR task domain. This paper summarizes results from empirical experimentation aimed at comparing traditional SBT strategies (e.g., Massed Exposure, Highlighting), and understanding the impact of Immersion, Presence, and Flow on performance. Relationships between Immersion, Presence, and Flow are explored and recommendations for future research are included.

This paper presents the evaluation of a Discrete Event Simulation model constructed to assist a teacher in the explanation of high school class content. The model was built using the free version of the Arena software after that teacher receives basic training for its use. The model was feasible, both financially and from the results presented. The results showed that the use of the simulation model can provide an increase in learning for students with more difficulty.

The objective of this work is to demonstrate the use of discrete event simulation to be employed as a didactic resource in automatic control systems classes. The application was made with the free and open-source software Ururau integrated to programmable logic controllers of manufacturing didactic stations. The result produced an environment for learning (simulation model and controllers). In this environment, the student can experience different practical situations with the simulation model and have direct contact with the real equipment.

The generation of insight from simulation models has received little attention in the discrete-event simulation (DES) literature. Often DES studies claim to have supported problem understanding and problem solving by creating new and effective ideas, however little empirical evidence exists to support these statements. This paper presents the design of an experimental study which aims to understand the role of simulation models in generating insights. Study participants are asked to solve a task based on a problem of a telephone service for non-emergency health care. One independent variable is manipulated: the features of the simulation model, forming three conditions. Participants either use the animation or only the statistical results of the model or no model at all to solve the task. The paper provides a preliminary analysis of the pilot tests, which indicates that simulation models may assist users in gaining better understanding and in achieving divergent thinking.

The benefits of on-demand computing capabilities, broad network access, maintainability, and multiplatform support are some of the essential characteristics that have made cloud computing the technology to adopt in recent years. While the technology has been used in simulation to some extent, it has not been widely available as it is expensive and complex. This paper reports on the design and development of a cloud-based discrete event simulator called ClouDES. ClouDES provides a platform for designing and executing discrete-event simulations with all the advantages of cloud computing. Its web- based easy to use interface attracts even non-expert users. As an example, the potential impact on non- experts users like students and especially middle and high school students are described. It is believed that students can be exposed to STEM concepts like probability, queuing, and functions while using technologies they are familiar with like mobile devices and social media.

Giving collegiate students an opportunity to participate in a multi-faceted development project remains a goal of many computer science programs, as well as modeling and simulation educational programs. A key feature of many complex simulation studies remains that distributed systems are relied on for their implementation, yet presenting such a system to students as a project is often overwhelming. Devising a simpler architecture, which involves not only distributed hardware, but also development of simulation software, and interoperability software, is the goal of the approach described here. As a work in progress, final results are not yet available, but principles and some foundational decisions are presented, with enough details such that the effort can be reproduced by other institutions.

This paper describes experiences in the first year of running final year Modeling & Simulation (M&S) undergraduate projects with The Hillingdon Hospitals, a large UK National Health Service (NHS) Hospital Trust. Our approach used project- and problem-based learning in a group context with emphasis on the student’s responsibility for the execution of their project. As part of their B.Sc. (HONS) Business Computing course, the students had taken a module on Business Process Modeling and Simulation during their second year. The student group was supported by two facilitators with help from two simulation researchers. Each student worked with a stakeholder in a variety of clinical service settings to create conceptual models, business process models and discrete-event simulations. The projects helped stakeholders to reflect on how their services might be improved, highlighted new areas of investigation, raised awareness of M&S at Hillingdon Hospital and equipped students with real-world M&S skills and experience.

This paper describes our experience teaching discrete-event simulation to several Engineering branches and Computer Science students. In our courses we emphasize the importance of conceptual modelling rather than the simulation tools used to build a model. We think that in discrete-event simulation university courses it is more important to provide knowledge to students to develop and analyze conceptual models than focusing in an specific simulation tool that will be industry dependent. Focusing in conceptual modelling with the support of a well-known simulation software provide the student with skills to create a model and then translate it to any simulator. Our courses use the Petri Net (PN) methodology by incrementing the complexity of models and PN using: Place-Transition PN, Timed PN and Colored Timed PN. A PN simulator is used to analyze the conceptual model and different rules and procedures are provided to match PN conceptual model to Arena simulation software.

In teaching simulation, the Buffon’s needle is a popular experiment to use for designing a Monte Carlo simulation to approximate the number π. Simulating the Buffon’s needle experiment is a perfect example for demonstrating the beauty of a Monte Carlo simulation in a classroom. However, there is a common misconception concerning the Buffon’s needle simulation. Erroneously, the simulation of the needle drop cannot be used to evaluate π. We have to simulate the needle’s angle from an uniform (0,π/2) distribution. It is self-referential in theory, since it requires the number π as the input value to approximate π. In this study, we propose a new method using the fixed-point iteration to remove the inherent paradox of the Buffon’s needle simulation. A new algorithm with Python implementation is proposed. The simulation outputs indicate that our new method is as good as if we use the true π value as an input.

Many business decisions can be ably supported by applications of various simulation types, including system dynamics, discrete event, agent based, and Monte Carlo. Many decisions involving large capital investments are made only after the proposed systems have been simulated and the expected return on investment verified using simulation. The applicability of simulation to business decisions would suggest that leading business schools would include teaching of simulation software in their curriculum. This paper reports on a survey of teaching of simulation software at leading business school. The prevalence teaching some simulation types over others and the reasons provided are discussed. Simulation community may want to consider the trends at business schools and the need to influence them.

This panel will discuss the state of the art in simulation optimization research and practice The participants include representation from both academia and industry: Michael Fu, chair (Maryland), Ilya Ryzhov, co-chair (Maryland), Güzin Bayraksan (Ohio State), Shane Henderson (Cornell), Barry Nelson (Northwestern), Warren Powell (Princeton), Ben Thengvall (OptTek Systems). The industry participant represents one of the leading software providers of optimization tools for simulation. An update of the online testbed of simulation optimization problems for the research community (http://www.simopt.org) will be provided.

General purpose graphics processing units (GPGPUs) suitable for general purpose programming have become sufficiently affordable in the last three years to be used in personal workstations. In this paper we assess the usefulness of such hardware in the statistical analysis of simulation input and output data. In particular we consider the fitting of complex parametric statistical metamodels to large data samples where optimization of a statistical function of the data is needed and investigate whether use of a GPGPU in such a problem would be worthwhile. We give an example, involving loss-given-default data obtained in a real credit risk study, where use of Nelder-Mead optimization can be efficiently implemented using parallel processing methods. Our results show that significant improvements in computational speed of well over an order of magnitude are possible. With increasing interest in "big data" samples the use of GPGPUs is therefore likely to become very important.

In literature, multi-objective particle swarm optimization (PSO) algorithms are shown to have potential in solving simulation optimization with real number decision variables and objectives. This paper develops a multi-objective PSO algorithm based on weighted scalarization (MPSOws) in which objectives are scalarized by different sets of weights at individual particles while evaluation results are shared among the swarm. Various scalarizing functions, such as simple weighted aggregation (SWA), weighted compromise programming (WCP), and penalized boundary intersection (PBI) can be applied in the algorithm; and to better achieve diversity and uniformity preservation of the Pareto set, a hybrid external archiving technique is proposed consisting of both KNN and ϵ-dominance methods. Numerical experiments on noise-free problems are conducted to show that MPSOws outperforms the benchmark algorithm and WCP is the most preferable strategy for the external archiving. In addition, simulation allocation rules (SARs) can be further applied with MPSOws when evaluation error is considered.

This paper is concerned with continuous simulation optimization problems with stochastic constraints. Thus both the objective function and constraints need to be estimated via simulation. We propose an Adaptive Search with Discarding and Penalization (ASDP) method for solving this problem. ASDP utilizes the penalty function approach from deterministic optimization to convert the original problem into a series of simulation optimization problems without stochastic constraints. We present conditions under which the ASDP algorithm converges almost surely, and conduct numerical studies aimed at assessing its efficiency.

Many procedures have been proposed in the literature to select the best from a finite set of alternatives. Among these procedures, frequentist procedures are typically designed under an indifference-zone (IZ) formulation, where an IZ parameter needs to be specified by users at the beginning of procedure. The IZ parameter is the smallest difference in the performance measure that the users care. In practice, however, the IZ parameter is often difficult to specify appropriately and may be specified in a conservative way (thus leading to excessive sampling effort). In this paper, we propose a frequentist IZ-free selection-of-the-best procedure. The procedure guarantees to select the best with at least a pre-specified probability of correct selection in an asymptotic regime. Through numerical studies, we show our procedure may out-perform IZ procedures, in terms of total sample size, when the IZ parameter is set conservatively or there are a large number of alternatives.

We consider the problem of identifying the system with the largest expected mean among a number of simulated systems. We provide a new fully sequential procedure whose continuation region is developed based on multivariate Brownian motion when the variances of the systems are known and equal. We provide an approximation to determine the procedure parameters and we show experimental results.

Traditional solutions to ranking and selection problems include two-stage procedures (e.g., the NSGS procedure of Nelson et al. 2001) and fully-sequential screening procedures (e.g., Kim and Nelson 2001 and Hong 2006). In a parallel computing environment, a naively-parallelized NSGS procedure may require more simulation replications than a sequential screening procedure such as that of Ni, Hunter, and Henderson (2013) (NHH), but requires less communication since there is no periodic screening. The parallel procedure NHH may require less simulation replications overall, but requires more communication to implement periodic screening. We numerically explore the trade-offs between these two procedures on a parallel computing platform. In particular, we discuss their statistical validity, efficiency, and implementation, including communication and load-balancing. Inspired by the comparison results, we propose a framework for hybrid procedures that may further reduce simulation cost or guarantee to select a good system when multiple systems are clustered near the best.

We consider a one-dimensional bisection method for finding the zero of a monotonic function, where function evaluations can be performed asynchronously in a parallel computing environment. Using dynamic programming, we characterize the Bayes-optimal policy for sequentially choosing points at which to query the function. In choosing these points, we face a trade-off between aggressively reducing the search space in the short term, and maintaining a desirable spread of queries in the long-term. Our results provide insight on how this trade-off is affected by function evaluation times, risk preferences, and computational budget.

The simulation of alternative evaluations in the ranking and selection problems often requires extensive amounts of computing power, so it is natural to use clusters with several workers for this task. We propose to extend the standard Knowledge Gradient policy to allow parallel and asynchronous dispatch of computation tasks among workers and denote it as the Asynchronous Knowledge Gradient. Simulation experiments indicate that performance loss due to parallelization of computations is below 25%. This implies that the proposed policy can yield significant benefits in terms of the time needed to obtain a desired approximation of the solution. We describe a master-slave architecture allowing for asynchronous dispatching of jobs among workers that handles problems with worker failures that are encountered in cluster environments. As a test bed of the procedure we developed an emulator of a heterogeneous computing cluster that allows testing of the parallel performance of stochastic optimization algorithms.

Distributed simulations require partitioning mechanisms to operate, and the best partitioning algorithms try to load-balance the partitions. Dynamic load-balancing, i.e., re-partitioning simulation environments at run-time, becomes essential when the load in the partitions change. In decentralised distributed simulation the information needed to dynamically load-balance seems difficult to collect and to our knowledge, all solutions apply a local dynamic load balancing: partitions exchange load only with their neighbours (more loaded partitions to less loaded ones). This limits the effect of the load-balancing. In this paper, we present a global dynamic load-balancing of decentralised distributed simulations. Our algorithm collects information in a decentralised fashion and makes re-balancing decisions based on the load processed by every logical processes. While our algorithm has similar results to others in most cases, we show an improvement of the load-balancing up to 30% in some challenging scenarios against only 12.5% for a local dynamic load-balancing.

We construct a discrete optimization via simulation (DOvS) procedure using discrete Gaussian Markov random fields (GMRFs). Gaussian random fields (GRFs) are used in DOvS to balance exploration and exploitation. They enable computation of the expected improvement (EI) due to running the simulation to evaluate a feasible point of the optimization problem. Existing methods use GRFs with a continuous domain, which leads to dense covariance matrices, and therefore can be ill-suited for large-scale problems due to slow and ill-conditioned numerical computations. The use of GMRFs leads to sparse precision matrices, on which several sparse matrix techniques can be applied. To allocate the simulation effort throughout the procedure, we introduce a new EI criterion that incorporates the uncertainty in stochastic simulation by treating the value at the current optimal point as a random variable.

Recently the stochastic kriging (SK) methodology proposed by Ankenman et al. (2010) has emerged as an effective metamodeling tool for approximating a mean response surface implied by a stochastic simulation. Although fruitful results have been achieved through bridging applications and theoretical investigations of SK, there lacks a unified account of efficient simulation experimental design strategies for applying SK metamodeling techniques. In this paper, we propose a sequential experimental design framework for applying SK to predicting performance measures of complex stochastic systems. This framework is flexible; i.e., it can incorporate a variety of design criteria. We propose several novel design criteria under the proposed framework, and compare the performance with that of classic non-sequential designs. The evaluation uses illustrative test functions and the well-known M/M/1 and the (s,S) inventory system simulation models.

We propose a new radial basis function (RBF) model for stochastic simulation, called regularized RBF (R-RBF). We construct the R-RBF model by minimizing a regularized loss over a reproducing kernel Hilbert space (RKHS) associated with RBFs. The model can flexibly incorporate various types of RBFs including those with conditionally positive definite basis. To estimate the model prediction error, we first represent the RKHS as a stochastic process associated to the RBFs. We then show that the prediction model obtained from the stochastic process is equivalent to the R-RBF model and derive the associated mean squared error. We propose a new criterion for efficient parameter estimation based on the closed form of the leave-one-out cross validation error for R-RBF models. Numerical results show that R-RBF models are more robust, and yet fairly accurate compared to stochastic kriging models.

The Greeks measure the rate of change of (financial) derivative prices with respect to underlying market parameters, which is essential in financial risk management. This paper focuses on a modified pathwise method that overcomes the difficulty of estimating Greeks with discontinuous payoffs as well as second-order Greeks and involves a kernel estimator whose accuracy/performance relies on a smoothing parameter (bandwidth). We explore the accuracy of the Greek delta, vega, and theta estimators of Asian digital options and up-and-out barrier call options with varying bandwidths. In addition, we investigate the sensitivity of a proposed iterative scheme that generates the "optimal" bandwidth. Our numerical experiments indicate that the Greek estimators are quite sensitive to the bandwidth choice, and the "optimal" bandwidth generated is sensitive to input parameters.

Many ranking-and-selection (R&S) procedures have been invented for choosing the best simulated system; in this paper we consider indifference-zone procedures that attempt to provide a probability of correct selection (PCS) guarantee. To obtain the PCS guarantee, existing procedures nearly always exploit knowledge about the particular combination of system performance measure (e.g., mean, probability, quantile) and assumed output distribution (e.g., normal, exponential, Poisson). In this paper we take a step toward general-purpose R&S procedures that work for many types of performance measures and output distributions, including situations in which different simulated alternatives have entirely different output distributions. There are only two versions of our procedure: with and without the use of common random numbers, and they can be applied to performance measures that can be expressed as expected values or quantiles. To obtain the desired PCS we exploit intense computation via bootstrapping, and establish the asymptotic PCS under very mild conditions.

Based on model-based methods, a recent class of stochastic search methods for nonlinear deterministic optimization, we propose a new algorithm for simulation optimization over continuous space. The idea is to reformulate the original simulation optimization problem into another optimization problem over the parameter space of the sampling distribution in model-based methods, and then use a direct gradient search on the parameter space to update the sampling distribution. To improve the computational efficiency, we further develop a two-timescale updating scheme that updates the parameter on a slow timescale and estimates the quantities involved in the parameter updating on a fast timescale. We provide numerical experiments to illustrate the performance of our algorithms.

The time required to execute simulation models of modern production systems remains high even with today’s computing power, particularly when what-if analyses need to be performed to investigate the impact of controllable system input variables on an output performance measure. Compared to mean and variance which are frequently used in practice, quantiles provide a more complete picture of the performance of the underlying system. Nevertheless, quantiles are more difficult to estimate efficiently through stochastic simulation. Stochastic kriging (SK) and quantile regression (QR) are two promising metamodeling tools for addressing this challenge. Both approximate the functional relationship between the quantile parameter of a random output (e.g., cycle time) and multiple input variables (e.g., start rate, unloading times). In this paper, we compare performances of SK and QR on steady-state quantile parameter estimation. Results are presented from simulations of an M/M/1 queue and a more realistic model of a semiconductor manufacturing system.

Consider a function that can only be evaluated with noise. Given estimates of the function values from simulation on a finite set of points, we seek a procedure to detect convexity or non-convexity of the true function on those points. We review an existing frequentist hypothesis test, and introduce a sequential Bayesian test. Our Bayesian test applies for both independent sampling and sampling with common random numbers, with known or unknown sampling variance. In each iteration, we collect a set of samples and update a posterior distribution on the true function values, and use that as the prior belief in our next iteration. We then approximate the probability that the function is convex based on the posterior using Monte Carlo simulation.

We consider the problem of multiple comparisons with a known standard, in which we wish to allocate simulation effort efficiently across a finite number of simulated systems, to determine which systems have mean performance exceeding a known threshold. We suppose that parallel computing resources are available, and that we are given a fixed simulation budget. We consider this problem in a Bayesian setting, and formulate it as a stochastic dynamic program. For simplicity, we focus on Bernoulli sampling, with a linear loss function. Using links to restless multi-armed bandits, we provide a computationally tractable upper bound on the value of the Bayes-optimal policy, and an index policy motivated by these upper bounds.

We present a multiple objective simulation optimization algorithm called multiple objective probabilistic branch and bound (MOPBnB) with the goal of approximating the efficient frontier and the associated Pareto optimal set in the solution space. MOPBnB is developed for both deterministic and noisy problems with mixed continuous and discrete variables. When the algorithm terminates, it provides a set of non-dominated solutions that approximates the Pareto optimal set and the associated objective function estimates that approximate the efficient frontier. The quality of the solutions is statistically analyzed using a measure of distance between solutions to the true efficient frontier. We also present numerical experiments with benchmark functions to visualize the algorithm and its performance.

Engineering design optimization often involves computationally expensive time consuming simulations. Although surrogate-based optimization has been used to alleviate the problem to some extent, surrogate models (like Kriging) struggle as the dimensionality of the problem increases to medium-scale. The enormity of the design space in higher dimensions (above ten) makes the search for optima challenging and time consuming. This paper proposes the use of probabilistic support vector machine classifiers to reduce the search space for optimization. The proposed technique transforms the optimization problem into a binary classification problem to differentiate between feasible (likely containing the optima) and infeasible (not likely containing the optima) regions. A model-driven sampling scheme selects batches of probably-feasible samples while reducing the search space. The result is a reduced subspace within which existing optimization algorithms can be used to find the optima. The technique is validated on analytical benchmark problems.

Simulation models of different fidelity levels are often available for a complex system. High-fidelity simulations are accurate but time-consuming. Therefore, they can only be applied to a small number of solutions. Low-fidelity simulations are faster and can evaluate a large number of solutions. But their results may contain significant bias and variability. We propose an Multi-fidelity Optimization with Ordinal Transformation and Optimal Sampling (MO2TOS) framework to exploit the benefits of high- and low-fidelity simulations to efficiently identify a (near) optimal solution. MO2TOS uses low-fidelity simulations for all solutions and then assigns a fixed budget of high-fidelity simulations to solutions based on low-fidelity simulation results. We show the benefits of MO2TOS via theoretical analysis and numerical experiments with deterministic simulations and stochastic simulations where noise is negligible with sufficient replications. We compare MO2TOS to Equal Allocation (EA) and Optimal Computing Budget Allocation (OCBA). MO2TOS consistently outperforms both EA and OCBA.

The expected opportunity cost is an important quality measure for the selection for the best simulated design among a set of design alternatives. It takes the case of incorrect selection into consideration and is particularly useful for risk-neutral decision makers. In this paper, we characterize the optimal selection rule which minimizes the expected opportunity cost by controlling the number of simulation replications allocated to each design. The observation noise of each design is allowed to have a general distribution. A comparison with other selection procedures in the numerical experiments shows the higher efficiency of the proposed method.

We consider the problem of finding a zero of an unknown function, for which we are only provided with noise-corrupted observations. Stochastic Approximation (SA) is the most popular method for solving such problems, but SA requires the practitioner to selectively tune arbitrary parameters to generate convergence behaviour, where the tuning is very application specific. We propose a fully sequential Monte Carlo sampling method which replaces the hard-to-tune parameters with an adaptively sampled estimator whose quality is carefully assessed at each iteration of the stochastic recursion using a relative width confidence interval on its optimality gap. Under mild conditions, the asymptotic behavior of the proposed method is investigated for various adaptive sampling rules.

This paper proposes uRace, a unified race algorithm for efficient offline parameter tuning of deterministic algorithms. We build on the similarity between a stochastic simulation environment and offline tuning of deterministic algorithms, where the stochastic element in the latter is the unknown problem instance given to the algorithm. Inspired by techniques from the simulation optimization literature, uRace enforces fair comparisons among parameter configurations by evaluating their performance on the same training instances. It relies on rapid statistical elimination of inferior parameter configurations and an increasingly localized search of the parameter space to quickly identify good parameter settings. We empirically evaluate uRace by applying it to a parameterized algorithmic framework for loading problems at ORTEC, a global provider of software solutions for complex decision-making problems, and obtain competitive results on a set of practical problem instances from one of the world's largest multinationals in consumer packaged goods.

Simulation-optimization has received a spectacular attention in the past decade. However, the theory still cannot meet the requirements from practice. Decision makers ask for methods solving a variety of problems with diverse aggregations and objectives. To answer these needs, the interchange of solution procedures becomes a key requirement as well as the development of (1) general modeling methodologies able to represent, extend and modify simulation-optimization as a unique problem, (2) mapping procedures between formalisms to enable the use of different tools. However, no formalism treats simulation-optimization as an integrated problem. This work aims at partially filling this gap by proposing a formalism based upon Event Relationship Graphs (ERGs) to represent the system dynamics, the problem decision variables and the constraints. The formalism can be adopted for simulation-optimization of control policies governing a queueing network. A Kanban Control System optimization is proposed to show the whole approach and its potential benefits.

Modeling and Simulation (M&S) as a discipline requires M&S Science to understand the general principles that build the foundations of M&S as a discipline, M&S Engineering to find general methods and solution patterns that can be applied to various problem domains, and M&S Applications to find real M&S-based solutions to real world problems. It also requires a deeper understanding of its epistemological and computational constraints from its students. The presentation addresses the mathematical foundations, the resulting philosophical implications, and looks into the future potential contributions of M&S in decision support on all levels. One of the main ideas is a paradigm shift from finding and supporting the best strategy towards allowing to take all alternatives into account and avoid bad and unstable alternatives.

Simulation is used increasingly throughout research, development, and planning for many purposes. While model output is often the primary interest, insights gained through the simulation process can also be valuable. Insights can come from building and validating the model as well as analyzing its behaviors and output; however, much that could be informative may not be easily discernible through these existing traditional approaches, particularly as models continue to increase in complexity.

This research extends current work in model analysis and program understanding to assist modelers in obtaining more insight into their models and the systems they represent. Results indicate these tools and techniques, when applied to even modest simulation models, can reveal aspects of those models not readily apparent to the builders or users of the models.

Population-based microsimulations often incorporate multiple data sources and modeling techniques. Results from these complex models should include uncertainty from model inputs. This work examines the confidence of reported results from population-based microsimulations using the Future Elderly Model, a dynamic microsimulation that forecasts health and economic outcomes for middle age and older adults. The primary objective is to determine whether systematic inclusion of uncertainty will disrupt the policy conclusions obtained from population-based microsimulation results.

This research presents a method to build large-scale agent-based modeling and simulation on PC for large-scale social systems with complex social networks. The novelty of this method is reflected in both designing of individual agent and organizing group of agents for interactions on a large scale. As a case study, we constructed a large-scale artificial city Beijing, with which to test policies for controlling the spread of disease among full population(19.6 million) in Beijing.