WSC 2006 Abstracts

Logistics, Transportation, and Distribution Track

Monday 10:30:00 AM 12:00:00 PM
Rail Simulation

Chair: Nicolas Lowes (University of Texas at Austin)

Simulation Services to Support the Control Design of Rail Infrastructures
Elisangela Mieko Kanacilo and Alexander Verbraeck (Delft University of Technology)

The design of rail infrastructure is a difficult task. Many parties are involved, and the tasks range from stakeholder issues to very detailed technical questions, such as control design. Simulation studies are often applied during infrastructure control system design, but the application of simulation is quite hard. One of the problems is the lack of flexibility in linking to information systems and databases. Another problem is that there are many potential users of the models, while most simulation systems can only be used by one user at a time. In addition, the tightly coupled structure of models makes model reuse and model maintenance hard. To overcome these problems, a service oriented simulation architecture is proposed for rail infrastructure modeling. The object-oriented simulation libraries that have been created within this architecture have been tested in a real project to estimate rail infrastructure capacity, and proved to work well.

Passenger Travel Behavior Model in Railway Network Simulation
Ting Li (RSM Erasmus University)

Transportation planners and public transport operators alike have become increasingly aware of the need to diffuse the concentration of the peak period travel. Differentiated pricing is one possible method to even out the demand and reduce peak load requirement. An evaluation of the potential effectiveness of strategies directed to flatten the demand distribution requires an understanding of the underlying factors that drive travel behavior (e.g., time-shifting, route change, mode change) with regard to price and service. In this paper, we present a Passenger Railway Network Simulation model with the intention of linking supply and demand. The objective is to evaluate the differentiated pricing impact on the passenger travel behavior, and consequently on the overall network performance, both financially and operationally. This paper focuses on the design and modeling approach of the Travel Behavior Model.

A Simulation Study for Designing a Rail Terminal in a Container Port
Byung Kwon Lee (Pusan National University), Jeong Hoon Seo (Research Center of Ubiquitous Port Logistics ), Soon Oh Park (VMS Solutions, Ltd.) and Bong Joo Jung and Kap Hwan Kim (Pusan National University)

Rail terminals in port container terminals play an important role for transshipping containers between rail wagons and port container terminals. This paper addresses a case study for designing a new rail terminal which is planned to be constructed in a port container terminal. A design process including an analytical calculation and a simulation study was proposed. The analytical approach was used to estimate the facility size and the simulation was used to evaluate proposed design alternatives in more detail. Design parameters were the number of transshipment lanes, the number of cranes, and the traffic flows inside the rail terminal

Monday 1:30:00 PM 3:00:00 PM
Transportation Simulation I

Chair: Michael Hunter (Georgia Institute of Technology)

A Comparison of Transportation Network Resilience Under Simulated System Optimum and User Equilibrium Conditions
Pamela M Murray-Tuite (Virginia Tech)

Resilience is a characteristic that indicates system performance under unusual conditions, recovery speed, and the amount of outside assistance required for restoration to its original functional state. Resilience is important for daily events, such as vehicle crashes, and more extreme events, such as hurricanes and terrorist attacks. Transportation resilience has ten dimensions: redundancy, diversity, efficiency, autonomous components, strength, collaboration, adaptability, mobility, safety, and the ability to recover quickly. This paper examines the influence of the system optimal and user equilibrium traffic assignments on the last four dimensions. No widely accepted measurement of resilience is available for transportation systems; this paper presents multiple metrics for the four examined contributing components that will aid future development of a single measure of resilience. An application of these measures to a test network found that user equilibrium results in better adaptability and safety while system optimum yields better mobility and faster recovery.

VISSIM: A Multi-parameter Sensitivity Analysis
Nicholas E. Lownes and Randy B. Machemehl (The University of Texas at Austin)

Traffic microsimulation is increasingly a preferred method of traffic analysis for today’s transportation professionals. The importance of properly calibrating these traffic simulations is evidenced by the adoption of microsimulation calibration standards by several state and federal transportation authorities. A component of the calibration process is the calibration of the simulation for capacity. Capacity is a high-level measurement that is a function of many lower-level user-defined input parameters. VISSIM utilizes psychophysical car following models that rely on ten user-defined parameters to represent freeway driving behavior. Several VISSIM driver behavior parameters have been shown to have a significant impact on roadway capacity. This paper seeks further understanding of the performance of the VISSIM traffic microsimulator by investigating the impact of driver behavior parameter combinations on a measure of freeway capacity. This paper is intended to provide insight useful for manual calibration of VISSIM microsimulation or the development of calibration algorithms.

An Investigation of Real-time Dynamic Data Driven Transportation Simulation
Michael Hunter, Richard Fujimoto, Wonho Suh, and Hoe Kyoung Kim (Georgia Institute of Technology)

Widespread deployment of sensors in roadways and vehicles is creating new challenges in effectively exploiting the wealth of real-time transportation system data. However, the precision of the real-time data varies depending on the level of data aggregation. For example, minute-by-minute data are more precise than hourly average data. This paper explores the ability to create an accurate estimate of the evolving state of transportation systems using real-time roadway data aggregated at various update intervals. It is found that simulation based on inflow data aggregated over a short time interval is capable of providing a superior representation of the real world over longer aggregate intervals. However, the perceived improvements are minimal under congested conditions and most pronounced under un-congested conditions. In addition, outflow constraints should be considered during congested flow periods, otherwise significant deviation from the real world performance may arise.

Monday 3:30:00 PM 5:00:00 PM
Supply Chain I

Chair: Pamela Murry-Tuite (Virginia Tech)

Linking Strategic Objectives to Operations: Towards a More Effective Supply Chain Decision Making
Changrui Ren, Jin Dong, Hongwei Ding, and Wei Wang (IBM China Research Laboratory)

Supply chain managers today face an unremitting challenge to their capabilities in both the volume and complexity of factors to be reconciled. In order to achieve more effective decision making, it is very necessary to link strategic objectives to operational actions. However, little is available to guide managers in translating a set of objectives into operations so far. This paper presents a comprehensive methodology to address this gap. In this methodology, strategic objectives are translated into performance metrics by qualitative strategy map and metric network firstly, and then quantitative techniques such as system dynamics simulation and optimization are adopted to take managers through the stages of strategy mapping, action evaluation and decision making. A case study, supported by a software tool, is carried out throughout the paper to illustrate how the method works.

Evaluating Refinery Supply Chain Policies and Investment Decisions Through Simulation-Optimization
Lee Ying Koo and Yuhong Chen (National University of Singapore), Arief Adhitya (Institute of Chemical and Engineering Sciences) and Rajagopalan Srinivasan and Iftekhar A Karimi (National University of Singapore)

The dynamic, non-linear, and complex nature of a supply chain with numerous interactions among its entities are best evaluated using simulation models. The optimization of such system is not amenable to mathematical programming approaches. The simulation-optimization method seems to be the most promising. In this paper, we look at a refinery supply chain simulation and attempt to optimize the refinery operating policies and capacity investments by employing a genetic algorithm. The refinery supply chain is complex with multiple, distributed, and disparate entities which operate their functions based on certain policies. Policy and investment decisions have significant impact on the refinery bottom line. To optimize them, we develop a simple simulation-optimization framework by combining the refinery supply chain simulator called Integrated Refinery In Silico (IRIS) and genetic algorithm. Results indicate that the proposed framework works well for optimization of supply chain policy and investment decisions.

Use of Federated Object Modeling to Develop a Macro-System Model
Mark F. Ruth (National Renewable Energy Laboratory) and Keith B. Vanderveen and Timothy J. Sa (Sandia National Laboratories)

The U.S. Department of Energy (DOE) is working on technology that could change our transportation fuel from gasoline to hydrogen. To assist in that effort, we are developing a macro-system model (MSM) that will link existing or developmental component models together to analyze crosscutting hydrogen issues. The MSM uses a federated simulation framework that extends the High Level Architecture (HLA). In this initial phase, three existing models have been linked to analyze two primary issues. The first issue we will examine will be the combined price of hydrogen production and delivery and the second will be a comparison of energy requirements and air emissions for multiple hydrogen production / delivery pathways (i.e., hydrogen produced from different feedstocks and transported via different means). Future work will involve linking other models to allow us to better analyze transition issues and making the MSM available to the hydrogen analysis community.

Tuesday 8:30:00 AM 10:00:00 AM
Supply Chain II

Chair: Jorge Pica (Lockheed Martin)

Observations on Material Flow in Supply Chains
Silvanus T. Enns (University of Calgary) and Pattita Suwanruji (Bantrel Company)

This paper summarizes one group of recent simulation studies comparing replenishment strategies. Time-phased planning, implemented using DRP and MRP logic, continuous-review reorder point (ROP) and single-card Kan-ban (KBN) systems are considered. These differ in terms of decision-making information, logic and integration requirements. Experimental results have been statistically analyzed and explained using simple stochastic models. Steps have also been taken to evaluate which strategies are most suitable under various demand patterns, levels of uncertainty and capacity constraints. Results show that DRP/MRP is superior under time-varying demand, regardless of whether or not capacity constraints are present. With no capacity constraints and level demand, ROP is superior to KBN, in part because it considers backorder information. With capacity constraints behavior is complicated by queuing effects. Under level demand, KBN may slightly outperform ROP, given assumptions of equal lot sizes, order placement delays and transportation times.

An Object-Oriented Framework for Simulating Multi-Echelon Inventory Systems
Manuel Rossetti, Mehmet Miman, Vijith Varghese, and Yisha Xiang (University of Arkansas)

In this paper, we discuss the design and use of an object-oriented framework for simulating multi-echelon inventory systems. We present a context for how the framework can be used through its application on two examples. In addition, we describe the design by examining the major conceptual artifacts within the object-oriented model. The framework is built on a Java Simulation Library (JSL) and permits easy modeling and execution of simulation models. The results and discussion indicate the flexibility and power of modeling with the framework. In addition, we summarize our future research efforts to model complex supply chains.

Earth to Orbit Logistics and Supply Chain Modeling and Simulation for NASA Exploration Systems
Mohamed Sam Fayez (Productivity Apex, Inc.), Mansooreh Mollaghasemi (University of Central Florida), Dayana Cope, Assem Kaylani, and Mike Callinan (Productivity Apex, Inc.) and Edgar Zapata (NASA Kennedy Space Center)

As exploration operations expand further into space, NASA must enhance its understanding and capability of the increasingly complex “supply chain” management of materials, people, information, and knowledge from sources (somewhere on Earth) to destinations (somewhere in space, e.g. LEO, GEO, Moon, Mars, etc.) and vice versa. Without the ability to understand, define, model, and simulate the supply chain to estimate, project, and affect decision making relevant to the supply chain performance, NASA will find it increasingly difficult to effectively manage this complex supply chain and to work as an informed collaborator with its supply chain partners in the planning, execution, and management of a successful space exploration mission. This paper describes an ongoing project on “the first ever application of 21st century space exploration supply chain modeling, simulation and analysis”.

Tuesday 10:30:00 AM 12:00:00 PM
Supply Chain III

Chair: Jorge Pica (Lockheed Martin)

Multi-Location Transshipment Problem with Capacitated Production and Lost Sales
Deniz Ozdemir (Universidad Autonoma de Nuevo Leon), Enver Yucesan (INSEAD) and Yale T. Herer (Technion)

We consider coordination among stocking locations through replenishment strategies that explicitly take into account lateral transshipments, i.e., transfer of a product among locations at the same echelon level. The basic contribution of our research is the incorporation of supply capacity into the traditional emergency transshipment model. We formulate the capacitated production case as a network flow problem embedded in a stochastic optimization problem. We develop a solution procedure based on infinitesimal perturbation analysis (IPA) to solve the stochastic optimization problem numerically. We analyze the impact on system behavior and on stocking locations' performance when the supplier may fail to fulfill all the replenishment orders and the unmet demand is lost. We find that depending on the production capacity, system behavior can vary drastically. Moreover, in a production-inventory system, we find evidence that either capacity flexibility (i.e., extra production) or transshipment flexibility is required to maintain a certain level of service.

Module-Based Modeling of Production-Distribution Systems Considering Shipment Consolidation
Xiaohua Wang and Soemon Takakuwa (Nagoya University)

A module-based modeling method was developed to analyze the production-distribution systems by using a discrete event simulation with ARENA. Excel VBA was also adopted to automatically generate the simulation programs. Using the proposed method, one can quickly create a multistage, multi-item supply chain system such as serials, convergent, divergent or general networks, for analyzing the performance of a supply chain. Both inventory control and shipment consolidation policy were considered in this study. A number of outputs can be used as a performance measure in the decision making; for example, transportation costs, inventory level and costs, and the fill rate. An actual application model was generated using the proposed method. The result shows that the module-based method is a powerful tool for modeling the supply chain systems.

A Simulation Framework for Real-Time Management and Control of Inventory Routing Decisions
Shrikant Jarugumilli, Scott E. Grasman, and Sreeram Ramakrishnan (University of Missouri-Rolla)

We consider a logistics network where a single warehouse distributes a single item to multiple retailers. Retailers in the network participate in a Vendor Managed Inventory (VMI) program with the warehouse, where the warehouse is responsible for tracking and replenishing the inventory at various retailer locations. The information update occurs every time a vehicle reaches a location and the decision on the delivery quantity and the next location to visit is made. For a small increase of locations in the network, the state space for the solution increases exponentially, making this problem NP-hard. Thus, we propose a solution methodology where in the size of the state space is reduced at each stage. In this work, we use simulation to develop the framework for the real-time control and management of inventory and routing decisions, given this scenario.

Tuesday 1:30:00 PM 3:00:00 PM
Transportation Simulation II

Chair: Pamela Murray-Tuite (Virginia Tech)

Application of Stochastic Optimization Method for an Urban Corridor
Ilsoo Yun and Byungkyu "Brian" Park (University of Virginia)

This paper presents a stochastic traffic signal optimization method that consists of the CORSIM microscopic traffic simulation model and a heuristic optimizer. For the heuristic optimizer, the performance of three widely used optimization methods (i.e., genetic algorithm, simulated annealing and OptQuest Engine) was compared using a real world test corridor with 12 signalized intersections in Fairfax, Virginia, USA. The performance of the proposed stochastic optimization method was compared with an existing signal timing optimization program, SYNCHRO, under microscopic simulation environment. The results indicated that the genetic algorithm-based optimization method outperforms the SYNCHRO program as well as the other stochastic optimization methods in the optimization of traffic signal timings for the test corridor.

A Systems Approach to Scalable Transportation Network Modeling
Kalyan Perumalla (Oak Ridge National Laboratory)

Emerging needs in transportation network modeling and simulation are raising new challenges with respect to scalability of network size and vehicular traffic intensity, speed of simulation for optimization, and fidelity of behavior for accurate capture of phenomena. Parallel execution is warranted to sustain the required detail, size and speed. However, few parallel simulators exist for such applications, partly due to the challenges underlying their development. Moreover, many simulators are based on time-stepped models, which can be computationally inefficient. Here an approach is presented to designing a simulator with memory and speed efficiency as the goals from the outset, and, specifically, scalability via parallel execution. The design makes use of discrete event modeling techniques as well as parallel simulation methods. Our simulator, called SCATTER, is being developed, incorporating such design considerations. Preliminary performance results are presented on benchmark road networks, showing scalability to one million vehicles simulated on one processor.

A Framework for New Generation Transportation Simulation
Daiheng Ni (University of Massachusetts Amherst)

This paper discussed the evolution and future trend of simulation in general domain and in transportation. Some challenges facing transportation modeling and simulation were identified. As an effort to address these challenges, a framework of new generation transportation simulation was developed. The framework is envisioned to be multi-scale in resolution, parallel in execution, and driven by objects. The paper further discussed strategies of transportation simulation at a nanoscopic level which offers a level of modeling detail beyond the state-of-the-art.

Tuesday 3:30:00 PM 5:00:00 PM
Manufacturing and Transport Systems

Chair: Daiheng Ni (University of Massachusetts)

Simulating the Operational Control of Free Ranging AGVs
Mark B Duinkerken, Tiemen Ter Hoeven, and Gabriel Lodewijks (Delft University of Technology)

Most transport systems using automated guided vehicles (AGVs) are centrally controlled and use fixed, pre-defined routes. Incidents cannot be handled as part of the common routine. Instead of using fixed path layouts, new trajectory planners for AGVs are developed that utilize the complete available space. To accommodate the increased flexibility, new operational controllers must be able to adapt to small deviations and incidents. In this paper an operational controller is presented that aims to satisfy two conflicting goals. First, the controller directs an AGV along a pre-planned trajectory with high accuracy. Second, the controller will avoid conflicts with static and dynamic obstacles. These conflicts are caused by small deviations between planned and realized paths, as well as by incidents like equipment failure. A simulation model is built to study the performance of this controller. The quality is compared to a PI-controller without collision avoidance characteristics.

Comparison of Routing Strategies for AGV Systems Using Simulation
Mark B Duinkerken, Gabriel Lodewijks, and Jaap A Ottjes (Delft University of Technology)

In automated transport systems, the origin-destination combinations are normally connected through a fixed layout, not representing the shortest path. The flexibility of these systems is limited and often the infrastructure is not optimally used. With the introduction of more powerful onboard computers and advanced sensor technology, the positioning and navigating possibilities of AGVs increased. However the routes, although virtual, are still fixed. A new step ahead would be to determine each path dynamically. This would use the free ranging capacities of AGVs to its full potential. In this paper, the benefits of the dynamic free ranging approach are investigated; a simulation model on the strategic level is presented that compares several common fixed layouts with the shortest connection approach. Naturally, the avoidance of collisions plays a central role. It is concluded that dynamic free ranging has high potential in terms of transport capacity of the resulting system.

Fast Creation of Realistic and Efficient Free Path Network Within a Simulation Model of a Shop Floor and a Supply Chain System
Michal Stec (Simulate First)

Good animation is invaluable to understanding and verifying the processes in a simulation model. In a shop floor and a supply chain simulation model, animation refers to animating movements of workers, forklifts or trucks entities between nodes. The large (thousands) number of possible connections in a complex model of at least 100 nodes requires a new approach to this undertaking. Instead of drawing separately connections between each and every node, a free path integrated network is needed that spans all nodes. This paper describes a technique developed for Arena (Rockwell Software) that builds automatically a realistic and integrated network among hundreds of nodes in a matter of minutes, giving the modeler practically unlimited field for further enhancement as far as the movement logic is concerned. Above that the created network "knows" the distances and shortest directions between each and every pair of nodes and navigates the free path object accordingly.

Wednesday 8:30:00 AM 10:00:00 AM
Transport and Data Collection

Chair: Jorge Pica (Lockheed Martin)

Assessment of Transport Infrastructure Projects by the Use of Monte Carlo Simulation: The CBA-DK Model
Kim Bang Salling and Steen Leleur (Centre for Traffic and Transport - Technical University of Denmark)

This paper presents the Danish CBA-DK software model for assessment of transport infrastructure projects. The assessment model is based on both a deterministic calculation following the cost-benefit analysis (CBA) methodology in a Danish manual from the Ministry of Transport and on a stochastic calculation, where risk analysis (RA) is carried out using Monte Carlo Simulation (MCS). After a description of the deterministic and stochastic calculations emphasis is paid to the RA part of CBA-DK with considerations about which probability distributions to make use of. Furthermore, a comprehensive assessment of the set of distributions are made. Finally conclusions and a perspective are presented.

An Object-Oriented Framework for Simulating Automatic Data Collection Systems
Manuel Rossetti and Bradley Hobbs (University of Arkansas) and Paul Faas (Air Force Research Laboratory)

In this paper, we discuss the design and use of a prototype object-oriented framework for simulating automatic data collection systems within their operational contexts. We motivate the purpose of the framework and how the framework can be used through the use of a simple scenario on an airbase. In addition, we overview the design by examining the major conceptual artifacts within the object-oriented model. The framework is built on a Java Simulation Library (JSL) and permits easy modeling and execution of simulation models. The results and discussion indicate the flexibility and power of modeling with the framework. Finally, we summarize our future research efforts to model more complicated automatic collection systems which include health monitoring systems within the Air Force’s new sense and respond logistics paradigm.

A Proposed Multiagent Model for Bus Crew Scheduling
Abdul Samad Shibghatullah, Tillal Eldabi, and Jasna Kuljis (Brunel University)

Bus crew scheduling is a complex problem to solve because of the large number of resources that need to be managed, complexity of allocating crew shifts, rising cost of crew and unpredictability of traffic and crew availability. This causes a difficulty to maintain an optimal schedule. Existing systems are excellent in producing optimal or near optimal schedules. However, to maintain such optimality for day-to-day operations, crew scheduling systems need to extend their capabilities by enabling crew reassignment, a feature that is not currently available in currently existing automated scheduling systems. The aim of this research is to model the crew reassignment process using agents and simulate agents’ behavior in order to establish ways of automating management of unpredictable events. The model should assist supervisor in managing everyday bus operations. The paper presents agents analysis and design using Gaia methodology.

Wednesday 10:30:00 AM 12:00:00 PM
Air Transportation and Maritime Simulation

Chair: Michael Hunter (Georgia Institute of Technology)

Airspace Geometry and 4D Flight Proximity Detection for Simulation of the National Airspace System
Paul T. R. Wang and Richard E. Snow (The MITRE Corporation)

The authors present uncomplicated and well established equations that can be used in simulation or real-world applications to determine key crossing points and aircraft proximity when the trajectory and speed of aircraft pairs are known. These equations, in closed form, were developed for computing the minimum distance between two aircraft within the four-dimensional (4D) space-time domain. The 4D flight proximity information can be used in simulation to evaluate large numbers of scheduled routes over a limited airspace for controller workload assessment. Also, it can be used to detect potential separation violations and impacts of traffic flow management (TFM) strategies. An example of computing the distance between two flights in 4D is presented. Sample aircraft proximity landscape in 4D space-time simulation with MATLAB code is also provided.

Estimating Operational Benefits of Aircraft Navigation and Air Traffic Control Procedures Using an Integrated Aviation Modeling and Evaluation Platform
Ralf H. Mayer (The MITRE Corporation)

Complex constraints generally define the performance of air transportation systems. These constraints include aircraft operational characteristics, airline operating procedures, and Air Traffic Control (ATC) requirements. The operational variability that is present in complex air transportation systems and their components typically demands a Monte Carlo approach when modeling system performance metrics. However, the inherent variability is generally not known a priori. This calls for a separate model validation approach that yields estimates of system variability and validates baseline model performance. This paper reports on an integrated aviation modeling platform that was developed for comparing and evaluating proposed aircraft flight operations and ATC procedures. It integrates both an agent-based Monte Carlo modeling environment and a data-driven model validation capability. The capabilities are outlined, the validation approach is described, and examples are presented of performance metrics quantifying operational benefits of air navigation procedures that are currently being implemented at major U.S. airports.

The Northwest Passage: A Simulation
Saran Somanathan, Peter Flynn, and Jozef Szymanski (University of Alberta)

Shipping from Yokohama to New York and St. Johns, Newfoundland is simulated by VSLAM for two routes: the Panama Canal, using fast and slow bluewater ships, and the Northwest Passage, using identically sized fast and slow Canadian Arctic Class (CAC) 3 ships. Each route is broken into a series of logical legs, and environmental conditions and wait times are assigned. Ice conditions are modeled from historical records. Average speed through the Northwest Passage shows little seasonal variation. Round trips per year are higher through the Northwest Passage for all cases in this study. The required freight rate (RFR) to recover all costs including capital recovery, is calculated for fast ships. RFR is lower for fast ships from St. Johns to Yokohama using the Northwest Passage, and higher for fast ships from New York to Yokohama. Possible future thinning of Arctic ice may improve the economics of the Northwest Passage.

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