WSC 2007 Final Abstracts

Ph.D. Colloquium Track

Sunday 1:00:00 PM 2:30:00 PM
Ph.D. Colloquium Luncheon

Chair: Young-Jun Son (University of Arizona)

Sunday 3:00:00 PM 6:00:00 PM
Ph.D. Colloquium Student Presentations

Chair: Simon Taylor (Brunel University)

The Database Monte Carlo Approach to Obtaining Effective Control Variates
Tarik Borogovac (Boston University)

The effectiveness of the method of control variates depends on the often challenging task of identifying controls that are highly correlated with the estimation variable. We introduce a generic approach for obtaining effective controls. The core idea of the approach is to extract information at nominal parameter values and use this information to gain estimation efficiency at neighboring parameters. We generate a database of appropriate random elements and construct effective (single or multiple) control variates using database evaluations at nominal parameters. This approach is closely related to the method of Common Random Numbers, and the problem of selecting an appropriate set of controls can be formulated as the selection of a desirable basis in a properly defined Hilbert space. Our experimental results have shown dramatic gains in computational efficiency in problems considered from the areas of computational finance, physics, and biology.

Simulation of Contact Centers
Eric Buist and Pierre L'Ecuyer (Université de Montréal)

This research project deals with the design and implementation of an efficient tool for simulating contact centers of various sizes and complexity. It also deals with realistic modeling of many aspects of contact centers not considered by most simulation tools. We will examine how to model aspects such as agents' absenteeism and non-adherence, recourse, etc., and perform sensitivity analyses to measure their impact on performance. We will also study variance reduction techniques, examine the issues they raise, implement them efficiently in the context of contact center simulation, and experiment with them to determine their impact on efficiency. We will also study and experiment with a combination of some variance reduction techniques. In particular, our study of stratification combined with control variates has revealed unexpected interaction effects and we examine ways of handling them. We are also experimenting with splitting methods to reduce the variance.

Using Flexible Points in a Developing Simulation
Joseph C. Carnahan and Paul F. Reynolds (University of Virginia)

Coercion is a semi-automated simulation adaptation technology that uses subject-matter expert insight about model abstraction alternatives, called flexible points, to change the behavior of a simulation. Coercion has been successfully applied to legacy simulations, but never before to a simulation under development. In this paper, we describe coercion of a developing simulation and compare it with our experience coercing legacy simulations. Using a simulation of selective dissolution in alloys as a case study, we observe that applying coercion early in the development process can be very beneficial, aiding subject matter experts in formalizing assumptions and discovering unexpected interactions. We also discuss the development of new coercion tools and a new language (Flex ML) for working with flexible points.

Stochastic Trust Region Gradient-Free Method (STRONG) -A New RSM-based Algorithm for Simulation Optimization
Kuo-Hao Chang (Purdue University)

Response Surface Methodology (RSM) is a metamodel-based optimization method. Its strategy is to explore small subregions of the parameter space in succession instead of attempting to explore the entire parameter space directly. This method has been widely used in simulation optimization. However, RSM has two significant shortcomings: Firstly, it is not automated. Human involvements are usually required in the search process. Secondly, RSM is heuristic without convergence guarantee. This paper proposes Stochastic Trust Region Gradient-Free Method (STRONG) for simulation optimization with continuous decision variables to solve these two problems. STRONG combines the traditional RSM framework with the trust region method for deterministic optimization to achieve convergence property and eliminate the requirement of human involvement. Combined with appropriate experimental designs and specifically efficient screening experiments, STRONG has the potential of solving high-dimensional problems efficiently.

Information Technology for Servicization of Cutting Tool Supply Chain
Chen Yang Cheng and Vittal Prabhu (Penn State University)

A new type of business model in the cutting tool supply chain has moved in the past several years from just providing selling cutting tools to also providing services. This servicization includes tool procurement management, quality control, inventory control, repair, and sharpening. The emergence of information technology, such as radio frequency identification (RFID) and web services, offers the possibility of enhancing the excutabilty and efficiency of these services. However, the multi-function combination of information technology potentially increases the complexity of service processes, and decreases the usability and system performance of these processes. In order to solve this problem, this paper analyzes the service process from three dimensions that evaluate usability, complexity and performance measurement. Each dimension addresses service processes from a different viewpoint which helps to analyze the business processes and further improve them through the analysis.

A Simulation and Optimization-based Approach for Railway Scheduling
Pavankumar Murali, Maged Dessouky, and Fernando Ordonez (University of Southern California)

We address the problem of routing and scheduling of freight trains on a complex railway network. The primary objectives are to minimize the delay in transporting shipments through a railway network, and to reject a shipment that could potentially overload the network. Simulation modeling is used to develop regression models that accurately estimate the travel time delay on a sub-network as a function of the trackage configuration, train type, and capacity and traffic on that sub-network. These delay estimation functions are fed into an integer programming model that suitably routes trains through a railway network based on the statistical expectation of running times in order to balance the railroad traffic and avoid deadlocks.

Parallel Cross-Entropy Optimization
Gareth Evans (University of Queensland)

The Cross-Entropy (CE) method is a modern and effective optimization method well suited to parallel implementations. There is a vast array of problems today, some of which are highly complex and can take weeks or even longer to solve using current optimization techniques. This paper presents a general method for designing parallel CE algorithms for Multiple Instruction Multiple Data (MIMD) distributed memory machines using the Message Passing Interface (MPI) library routines. We provide examples of its performance for two well-known test-cases: the (discrete) Max-Cut problem and (continuous) Rosenbrock problem. Speedup factors and a comparison to sequential CE methods are reported.

Simulating Gang Violence In An Asymmetric Environment
Sam Huddleston and Jon Fox (UVA)

The United States is entering the fourth year of the Operation Iraqi Freedom (OIF) and continues to commit resources at an alarming rate. And for most of us reading or watching the major media sources, one of our questions remains “will any plan work in Iraq?” Based on recent literature examining the similarities between insurgency and gang violence, this study will attempt to structure an agent based simulation for modeling gang crime within a US city. The external adjustments to quality of life options and law enforcement staffing offer the potential to improve understanding of asymmetric environment.

Allocation of Simulation Runs for Simulation Optimization
Alireza Kabirian and Sigurdur Olafsson (Department of Industrial and Manufacturing Systems Engineering, Iowa State University)

Simulation optimization (SO) is the process of finding the optimum design of a system whose performance measure(s) are estimated via simulation. We propose some ideas to improve overall efficiency of the available SO methods and develop a new approach that primarily deals with continuous two dimensional problems with bounded feasible region. Our search based method, called Adaptive Partitioning Search (APS), uses a neural network as meta-model and combines various exploitation strategies to locate the optimum. Our numerical results show that in terms of the number of evaluations (simulation runs) needed, the APS algorithm converges much faster to the optimum design than two well established methods used as benchmark.

Enhanced Modeling Using Entities In An Integrated Process-Driven and Event-Driven Environment
Vishnu S. Kesaraju (Wright State University)

In process-driven simulation models, the system can be represented by blocks or system networks through which entities flow to mimic real life system objects. In event-driven models, the system can be represented by event graphs, which focus on the abstraction of the event rather than on observable physical entities. A new simulation framework that integrates process- and event-driven approaches offers a powerful combination of tools to the modeler. The integrated Entity/Event (IE2) framework has two main components: an E2 (Entity/Event) Integrator and an IE2 model. One of the main goals for the design of the framework is to preserve the elegantly simple logic to process events, even when processing of entities is taking place simultaneously. An important feature of standard event graphs is parameterization of event vertices. The framework based on an integrated entity/event approach has been further enhanced to allow parameterization by explicitly representing entities at the event-driven level.

Importance Sampling Estimation for the Probability of Overflow in Serve the Longest Queue System
Kevin Leder (Brown University)

An importance sampling algorithm for estimating the probability that at least one queue overflows in the serve the longer queueing system is presented. We prove that this algorithm is asymptotically efficient, in order to show this it is necessary to find an explicit formula for the large deviation rate of the rare event of interest. Therefore we also explicitly identify the large deviations rate for the probability of buffer overflow. The results presented in this paper hold for an arbitrary number of queues being served by the server. The only restriction placed on the arrival and service rates is that the system be stable.

Simulation-based Decision Making for Maintenance Policy Selection for Complicated Systems
Zhaojun Li (Wichita State University)

This paper investigates the performance of degrading systems under structural constraints applying discrete event simulation. The maintenance polices under consideration for such system are minimum repair, failure replacement and preventive maintenance. The system performance is measured in terms of two criteria, long-term availability and average cost rate, and the optimal maintenance policy is selected based on the two criteria using compromise programming method. Unlike many methods formulating the maintenance problem as Markov processes by assuming exponentially distributed mean time to failure and mean time to repair, the simulation model can deal with most time distributions such as Weibull and Lognormal, which are more practical in real application. Due to the intractability of Markovian formulation and the ease of obtaining performance indices by simulation, the simulation method is an effective tool to facilitate decision making. The simulation model is validated by comparing simulation results with analytical results from a Markovian formulation.

Classification Analysis for Simulation of Machine Breakdowns
Lanting Lu, Christine S. M. Currie, and Russell C. H. Cheng (University of Southampton) and John Ladbrook (Ford Motor Company)

Machine failure is often an important factor in throughput of manufacturing systems. To simplify the inputs to the simulation model for complex machining and assembly lines, we have derived the Arrows classification method to group similar machines, where one model can be used to describe the breakdown times for all of the machines in the group and breakdown times of machines can be represented by finite mixture model distributions. The Two-Sample Cramer-von Mises statistic is used to measure the similarity of two sets of data. We evaluate the classification procedure by comparing the throughput of a simulation model when run with mixture models fitted to individual machine breakdown times; mixture models fitted to group breakdown times; and raw data. Details of the methods and results of the grouping processes will be presented, and will be demonstrated using an example.

Semi-Automatic Simulation Component Reuse
Yariv N. Marmor and Avigdor Gal (Technion - Israel Institute of Technology)

Simulation reuse is a special case of code reuse, where a developer writes a component once and can then reuse it. However, two main characteristics differentiate it from other types of code reuse: (1) Simulation code, in many cases, is built by non-expert developers. (2) Simulation may be used in many completely different application areas, if only the similarity of its components can be recognized. In this work, we aim at improving simulation reuse by providing a technique for recognizing similarities among simulation pieces of code. We offer a methodology for semi-automatic support for the process of simulation component reuse. Our methodology is based on a table-based modeling of simulation components, hierarchical clustering of existing components and then a careful walk-through of a designer through the hierarchy for the identification of relevant components. To illustrate our approach, we make use of three real-world case studies involving resource scheduling.

A Longitudinal Study of the Impact of Information Technology on Organizational Knowledge Management Processes Using Agent-based Modeling
Srikanth Mudigonda (University of Missouri-St.Louis)

Prior research on knowledge management (KM) and its relationship with information technologies (ITs) and organizational performance has typically been conducted using cross-sectional studies under a limited set of environmental and organizational conditions. Consequently, the longitudinal impact of ITs on the relationships among KM processes, individual and organizational knowledge, and performance has not been investigated. Synthesizing the literature drawn from cognitive science, artificial intelligence, computational modeling of organizations, and organizational behavior, the proposed dissertation will investigate these relationships over an extended period. It will use agent-based modeling, with distinct representations of: knowledge, KM processes (e.g., socialization, exchange), organizational tasks, and how task performance benefits from KM. By studying these aspects before and after the introduction of different ITs, the effect of ITs on KM and firm performance will be examined. Field interviews at organizations will be used to validate the model.

Hierarchical Planning and Multi Level Scheduling for Simulation-based Probabilistic Risk Assessment
Hamed S. Nejad (Center for Risk and Reliability, University of Maryland, College Park) and Ali Mosleh (Center for Risk and Reliability)

Simulation of dynamic complex systems, specifically those comprised of large numbers of components with stochastic behaviors, for the purpose of probabilistic risk assessment, faces challenges in every aspect of the problem. Scenario generation confronts many impediments, one being the problem of handling the large number of scenarios without compromising completeness. Probability estimation and consequence determination processes must also be performed under real world constraints on time and resources. In the approach outlined in this paper, hierarchical planning is utilized to generate a relatively small but complete group of high level risk scenarios to represent the unsafe behaviors of the system. Multi-level scheduling makes the probability estimation and consequence determination processes more efficient and affordable. The scenario generation and scheduling processes both benefit from an updating process that takes place after a number of simulation runs by fine-tuning the scheduler's level adjustment parameters and refining the planner's high level system model.

Phased Approach to Simulation of Security Algorithms for Ambient Intelligent (AmI) Environments
Muaz Niazi (Foundation University, FUIMCS) and Abdul Rauf Baig (National University-FAST)

The finalization of AmI simulation requires actual installation of sensors and actuators in the real-world. On one hand, having to model a system with an eventual extensive human user interaction, makes modeling difficult. On the other hand, building an effective simulation before actual sensor deployment, is an important requirement for success of the perceived system. In this work, we focus on creation of an effective simulation for algorithms for security in ambient intelligent environments. We propose using a phased approach to simulation design in AmI environments. The first phase involves focusing on a model of an effective agent-based simulation is developed for the algorithms. Next, interface is developed for the simulation tools and the hardware which includes the sensors and the actuators. Finally, the simulation is executed in the real-world environment. As a case study, we present a simulation of an algorithm for authentication-free algorithm for open access resources.

Code Analysis and CS–XML
Kara A. Olson and C. Michael Overstreet (Old Dominion University) and E. Joseph Derrick (Radford University)

The automated analysis of model specifications is an area that historically receives little attention in the simulation research community but which can offer significant benefits. A common objective in simulation is enhanced understanding of a system; model specification analysis can provide insights not otherwise available as well as time and cost savings in model development. The Condition Specification (CS) (Overstreet and Nance 1985) represents a model specification form that is amenable to analysis. This paper discusses the motivations for and the creation of CS-XML; a translator for CSes into XML-based Condition Specifications; and a translator for CS-XML into fully-executable C/C++ code. It presents initial results from analysis efforts using CodeSurfer (Anderson et al. 2003), a software static analysis tool, and discusses future work. In conclusion, it is argued that CS-XML can provide an essential foundation for Web Services that support the analysis of discrete-event simulation models.

Agent Based Simulation Model to Predict Performance of Teams Working Under Unstructured Job Environments.
Jose A Rojas (Florida International University/ Universidad del Turabo) and Ronald Giachetti (Florida International University)

The focus of this research is to develop a computational tool to study and design teams working under complex job environments. The Team Coordination model is an agent-based simulation model developed to study coordination and performance of teams. The job structure is modeled as a conditional network, in which some tasks are the result of probabilistic outcomes of predecessor tasks and tasks duration are random variables. The simulation model will be used as a tool to determine which team design configuration will perform best on a particular job.

Appraisal of Airport Alternatives in Greenland by the use of Risk Analysis and Monte Carlo Simulation
Kim Bang Salling and Steen Leleur (Technical University of Denmark)

The research to be presented consists of an appraisal study of three airport alternatives in Greenland by the use of an adapted version of the Danish CBA-DK model. The assessment model is based on both a deterministic calculation by the use of conventional cost-benefit analysis and a stochastic calculation, where risk analysis is carried out using Monte Carlo simulation. The feasibility risk adopted in the model is based on assigning probability distributions to the uncertain model parameters. Two probability distributions are presented, the Erlang and normal distribution respectively assigned to the construction cost and the travel time savings. The obtained model results aim to provide an input to informed decision-making based on an account of the level of desired risk as concerns feasibility risks. This level is presented as the probability of obtaining at least a benefit-cost ratio of a specified value. Finally, some conclusions and a perspective are presented.

Agent-based Simulation Framework for Supply Chain Planning in the Lumber Industry
Luis Antonio Santa Eulalia and Sophie D'Amours (Universite Laval) and Jean-Marc Frayret (Ecole Polytechnique de Montreal and FOR@C Research Consortium)

Agent-based simulation is considered a promising approach for supply chain (SC) planning. Although there have been many relevant advances on how to specify, design, and implement agent-based simulation systems for SC planning, the related literature does not thoroughly address the analysis phase. In this early phase, simulation stakeholders discuss and decide which kind of simulation experiments have to be performed and their requirements. Consequently, it considerably influences the whole development process and the resulting simulation environment. Thus, this work proposes an agent-based simulation framework for modeling SC planning systems in the analysis phase. In addition, it proposes a formal method for converting the analysis model into specification and design models. Another contribution of this work is the instantiation of the proposed framework into a particular model of the lumber industry. This model is then validated by means of an agent-based simulation platform being developed for this industry sector in Canada.

A New Method for Reverse Engineering the Visual System
Diglio A. Simoni (RTI International)

Arguably our understanding of the world is based to a very large extent on our visual perceptual abilities. We describe a new type of robust perception-based image analysis system derived from psychophysical observations of eye scan patterns of expert observers. Psychophysical metrics that describe the visual system's real-time selection of image regions during active visual search processes are recast as fuzzy predicates to form the foundation of a rule set that simulates the perceptual and cognitive strategies used by the expert observers. This results in a simulated search mechanism composed of a bottom-up neural network-based sensory processing model coupled with a top-down fuzzy expert system model of search decision processes that helps redesign and perfect the supervised and unsupervised machine analysis of work-related or research imagery. The use of supercomputing resources for this research is highlighted.

IBatch: An Autoregressive—Batch-Means Procedure for Steady-State Simulation Output Analysis
Ali Tafazzoli (North Carolina State University)

We develop IBatch, a new procedure for steady-state simulation output analysis which can be considered as an extension of the classical method of nonoverlapping batch means. IBatch addresses the correlation, nonnormality, and start-up problems by exploiting the properties of a first-order autoregressive time series model of the suitably truncated batch means with a sufficiently large batch size. This approach yields an approximately unbiased estimator of the variance of the batch means as well as an asymptotically valid confidence interval for the steady-state mean of the underlying output process. An experimental performance evaluation demonstrates the potential of IBatch to provide a completely automated, robust, and transparent method for steady-state simulation output analysis. Major Advisor: Dr. James R. Wilson

Discrete Stochastic Optimization using Simplex Interpolation
Honggang Wang and Bruce W. Schmeiser (Purdue University)

Optimizing a stochastic system with a set of discrete design variables is an important and difficult problem. Much recent research has developed efficient methods for stochastic problems where the objective functions can only be estimated by simulation oracles. Due to the expense of simulation and typical large search space, most of the present approaches are either non-convergent or converge slowly. We propose a method using continuous search with simplex data interpolation to solve a wide class of discrete stochastic optimization problems. Adopting simplex interpolation for the discrete stochastic problem, we create a continuous piecewise-linear stochastic optimization problem. A retrospective framework provides a sequence of deterministic approximating problems that can be solved using continuous optimization techniques such as bundle methods or Shor's r-algorithm that guarantee desirable convergence properties. Numerical experiments show that our method finds the optimal solution orders of magnitude faster than random search algorithms, including the recently developed COMPASS.

Simulating Soundscape Evaluations in Urban Open Spaces
Lei Yu and Jian Kang (Sheffield University)

Urban open spaces play a dramatic role in current urban renaissance. These spaces are important for healthy life and attract strong public interests. As a part of physical environment, acoustic comfort is an essential component to be considered by city designers and acousticians. Subjective evaluations of acoustic comfort are crucial in the acoustic design process. How-ever, it is difficult to predict the subjective evaluations, as there are a large number of variables in terms of the physical and social environments which could affect the evaluations. In this study, therefore, artificial neural network (ANN) techniques have been introduced to predict subjective evaluations of acoustic comfort and sound level annoyance. In this presentation, the modeling process is illustrated and the results are discussed. It is shown that the ANN approach is an efficient way to predict subjective evaluations at the design stage.

Sunday 6:00:00 PM 7:30:00 PM
Ph.D. Colloquium Posters

Chair: Wai Kin Chan (Rensselaer Polytechnic Institute)

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