John A. Sokolowski, PHD, is Executive Director of the Virginia Modeling, Analysis, and Simulation Center (VMASC) at Old Dominion University, where he is also Associate Professor of Modeling and Simulation Engineering. He is the coeditor of Principles of Modeling and Simulation: A Multidisciplinary Approach, Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains, and Modeling and Simulation in the Medical and Health Sciences and coauthor of Modeling and Simulation for Analyzing Global Events, all published by Wiley.Catherine M. Banks, PHD, is Research Associate Professor at VMASC. She is the coeditor of Principles of Modeling and Simulation: A Multidisciplinary Approach, Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains, and Modeling and Simulation in the Medical and Health Sciences and coauthor of Modeling and Simulation for Analyzing Global Events, all published by Wiley.

• Contributors xiiiPreface xviiIntroduction 11 Research and Analysis for Real-World Applications 8Catherine M. Banks1.1 Introduction and Learning Objectives  81.1.1 Learning Objectives  101.2 Background  101.3 M&S Theory and Toolbox  131.3.1 Simulation Paradigms  151.3.2 Types of Modeling  161.3.3 Modeling Applications  171.4 Research and Analysis Methodologies  18Case Study: A Methodology for M&S Project Progression  20Summary  23Key Terms  24Exercises  25References  252 Human Behavior Modeling: A Real-World Application 26John A. Sokolowski2.1 Introduction and Learning Objectives  262.2 Background and Theory  272.2.1 Classical Decision Theory  272.2.2 Naturalistic Decision Making  312.2.3 Recognition-Primed Decision Model  332.2.4 Military Decision Making  372.2.5 Computational Techniques for Implementing the CJTF Decision Process  402.2.6 Summary of the State-of-the-Art  53Case Studies  54Summary  81Key Terms  82Exercises  83References  83Appendix: A Decision Scenario and Associated Data  883 Transportation 93R. Michael Robinson3.1 Introduction and Learning Objectives  933.2 Background  943.3 Theory  953.3.1 Simulation Levels  953.3.2 Traffic Analysis Zones  973.3.3 The Four-Step Model  983.3.4 Method of Successive Averages  1023.3.5 Volume Delay Functions  1053.3.6 Dynamic Traffic Assignment  1083.4 Transportation Modeling Applications  1133.4.1 Traffic Demand Models  1133.4.2 Public Transportation Models  1143.4.3 Freight Modeling  1173.4.4 Evacuation Simulations  121Summary  124Key Terms  125Exercises  126References  126Further Reading  1274 Homeland Security Risk Modeling 129Barry C. Ezell4.1 Introduction and Learning Objectives  1294.2 Background  1314.2.1 Bioterrorism Risk Assessment 2006  1324.2.2 Estimating Likelihood of Terrorist Events  1334.2.3 Risk Assessed as a Function of Threat  Vulnerability  and Consequence  1354.3 Theory and Applications in Risk Modeling  1364.3.1 Philosophical Considerations  1374.3.2 Ontology and Epistemology  1384.3.3 Issues and Implications for the Risk Analyst  1384.3.4 Philosophical Considerations Summary  1414.3.5 System Principals and Applications for the Risk Analyst  1424.3.6 Factors in Developing a Risk Assessment Study Plan  1434.3.7 Scope and Bound in a Risk Study: Constraints  Limitations  and Assumptions  1454.3.8 Well-Known Challenge in Homeland Security Studies  1464.4 Elements of a Study Plan  1474.5 Modeling Paradigms  1484.5.1 Simple Verses Complex Methodologies  1484.5.2 Quantitative and Qualitative Designs  1484.5.3 Modeling Approaches and Examples  1504.5.4 Verification and Validation for Risk Models  156Case Studies  157Summary  161Key Terms  161Exercises  161References  162Further Reading  1645 Operations Research 165Andrew J. Collins and Christine S.M. Currie5.1 Introduction and Learning Objectives  1655.2 Background  1665.2.1 OR Techniques  1685.3 Theory  1695.3.1 Problem Structuring Methods  1695.3.2 Queuing Theory  1755.3.3 Decision Analysis  1795.3.4 Game Theory  1825.3.5 Optimization  1865.4 Modeling Paradigms  192Case Studies  193Summary  199Key Terms  201Exercises  202x ContentsReferences  204Further Reading  2066 Business Process Modeling 207Rafael Diaz  Joshua G. Behr  and Mandar Tulpule6.1 Introduction and Learning Objectives  2076.2 Background  2076.3 Discrete-Event Simulation  2146.3.1 Introduction  2146.3.2 Fundamentals  2156.3.3 Queuing System Model Components  2186.3.4 Time Advance Mechanism  2196.3.5 Simulation Flowchart  2206.4 Discrete-Event Simulation Case Study  2216.4.1 Introduction  2226.4.2 Background  2226.4.3 Research Question  2236.4.4 Overview of Optimization Model  2246.4.5 The Simulation Model  2256.4.6 Experimental Setting  2256.4.7 Simulation Parameterization and Execution  2266.4.8 Weigh Zones and Product Reassignment  2266.4.9 Results  2266.5 System Dynamics Simulation  2276.5.1 Introduction  2276.5.2 Fundamentals  2286.5.3 The Stock and Flow Diagrams  2296.5.4 Model Calibration  2316.5.5 Model Testing  2336.5.6 Population Modeling Exercise  2336.5.7 Application of System Dynamics  2356.5.8 Background  2356.5.9 Research Question  2386.5.10 Dynamic Hypothesis  2386.5.11 Causal Loop Diagram  2386.5.12 Stock and Flow Model  2396.5.13 Simulation and Results  2406.5.14 Conclusions  2446.6 Monte Carlo Simulation  2446.6.1 Introduction  2446.6.2 Fundamentals  2456.6.3 Probability Theory and Monte Carlo  2476.6.4 Central Limit Theorem  2476.6.5 Three-Sigma Rule  2476.6.6 Monte Carlo Case Study  2496.6.7 Research Question  2506.6.8 Model Parameters  2506.6.9 Simulation Procedure  2506.6.10 Estimating Profit  2516.6.11 Excel Implementation  2536.6.12 Outcomes  2536.6.13 Conclusions  254Summary  255Key Terms  255Review Questions  256References  2577 A Review of Mesh Generation for Medical Simulators 261Michel A. Audette  Andrey N. Chernikov  and Nikos P. Chrisochoides7.1 Introduction and Learning Objectives  2617.2 Background—A Survey of Relevant Biomechanics and Open-Source Software  2637.2.1 Architecture of an Interactive Medical Simulator  2637.2.2 Mechanics of Tissue Manipulation in Medical Simulation  2647.2.3 Mechanics of Tissue Cutting and Resection in Medical Simulation  2697.2.4 Open-Source Resources in Medical Simulation  2697.3 Theory—The Impact of Element Quality and Size on Simulation  2727.4 Modeling Paradigms—Methods for Mesh Generation  2767.4.1 Structured Tetrahedral Mesh Generation  2767.4.2 Unstructured Tetrahedral Mesh Generation  2767.4.3 Octree-Based Unstructured Tetrahedral Mesh Generation  2797.4.4 Delaunay Unstructured Tetrahedral Mesh Generation  2807.4.5 Advancing Front Unstructured Tetrahedral Mesh Generation  2847.4.6 Optimization-Based Unstructured Tetrahedral Mesh Generation  2847.4.7 Unstructured Surface Mesh Generation  285Case Studies  289Summary  291Key Terms  292Acknowledgments  293Exercises  293References  2948 Military Interoperability Challenges 298Saikou Y. Diallo and Jos´e J. Padilla8.1 Introduction and Learning Objectives  2988.2 Background  2998.2.1 Overview  3008.2.2 State of the Art in Interoperability  3008.2.3 Levels of Interoperability  3028.2.4 Current Approaches to Interoperation  3038.3 Theory  3058.3.1 Data Models  3068.3.2 A Relational Model of Data in M&S Systems  307Case Study: Live Virtual Constructive Simulation Environments  3118.4 Live Virtual Constructive  3118.5 LVC Examples  3158.6 Distributed Simulation Engineering and Execution Process (DSEEP)  3168.7 LVC Architecture Framework (LVCAF)  3208.8 Simulation Systems  322Summary  323Key Terms  324Exercises  325References  325Index 329

John A. Sokolowski, PHD, is Executive Director of the Virginia Modeling, Analysis, and Simulation Center (VMASC) at Old Dominion University, where he is also Associate Professor of Modeling and Simulation Engineering. He is the coeditor of Principles of Modeling and Simulation: A Multidisciplinary Approach, Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains, and Modeling and Simulation in the Medical and Health Sciences and coauthor of Modeling and Simulation for Analyzing Global Events, all published by Wiley.Catherine M. Banks, PHD, is Research Associate Professor at VMASC. She is the coeditor of Principles of Modeling and Simulation: A Multidisciplinary Approach, Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains, and Modeling and Simulation in the Medical and Health Sciences and coauthor of Modeling and Simulation for Analyzing Global Events, all published by Wiley.

• Contributors xiiiPreface xviiIntroduction 11 Research and Analysis for Real-World Applications 8Catherine M. Banks1.1 Introduction and Learning Objectives  81.1.1 Learning Objectives  101.2 Background  101.3 M&S Theory and Toolbox  131.3.1 Simulation Paradigms  151.3.2 Types of Modeling  161.3.3 Modeling Applications  171.4 Research and Analysis Methodologies  18Case Study: A Methodology for M&S Project Progression  20Summary  23Key Terms  24Exercises  25References  252 Human Behavior Modeling: A Real-World Application 26John A. Sokolowski2.1 Introduction and Learning Objectives  262.2 Background and Theory  272.2.1 Classical Decision Theory  272.2.2 Naturalistic Decision Making  312.2.3 Recognition-Primed Decision Model  332.2.4 Military Decision Making  372.2.5 Computational Techniques for Implementing the CJTF Decision Process  402.2.6 Summary of the State-of-the-Art  53Case Studies  54Summary  81Key Terms  82Exercises  83References  83Appendix: A Decision Scenario and Associated Data  883 Transportation 93R. Michael Robinson3.1 Introduction and Learning Objectives  933.2 Background  943.3 Theory  953.3.1 Simulation Levels  953.3.2 Traffic Analysis Zones  973.3.3 The Four-Step Model  983.3.4 Method of Successive Averages  1023.3.5 Volume Delay Functions  1053.3.6 Dynamic Traffic Assignment  1083.4 Transportation Modeling Applications  1133.4.1 Traffic Demand Models  1133.4.2 Public Transportation Models  1143.4.3 Freight Modeling  1173.4.4 Evacuation Simulations  121Summary  124Key Terms  125Exercises  126References  126Further Reading  1274 Homeland Security Risk Modeling 129Barry C. Ezell4.1 Introduction and Learning Objectives  1294.2 Background  1314.2.1 Bioterrorism Risk Assessment 2006  1324.2.2 Estimating Likelihood of Terrorist Events  1334.2.3 Risk Assessed as a Function of Threat  Vulnerability  and Consequence  1354.3 Theory and Applications in Risk Modeling  1364.3.1 Philosophical Considerations  1374.3.2 Ontology and Epistemology  1384.3.3 Issues and Implications for the Risk Analyst  1384.3.4 Philosophical Considerations Summary  1414.3.5 System Principals and Applications for the Risk Analyst  1424.3.6 Factors in Developing a Risk Assessment Study Plan  1434.3.7 Scope and Bound in a Risk Study: Constraints  Limitations  and Assumptions  1454.3.8 Well-Known Challenge in Homeland Security Studies  1464.4 Elements of a Study Plan  1474.5 Modeling Paradigms  1484.5.1 Simple Verses Complex Methodologies  1484.5.2 Quantitative and Qualitative Designs  1484.5.3 Modeling Approaches and Examples  1504.5.4 Verification and Validation for Risk Models  156Case Studies  157Summary  161Key Terms  161Exercises  161References  162Further Reading  1645 Operations Research 165Andrew J. Collins and Christine S.M. Currie5.1 Introduction and Learning Objectives  1655.2 Background  1665.2.1 OR Techniques  1685.3 Theory  1695.3.1 Problem Structuring Methods  1695.3.2 Queuing Theory  1755.3.3 Decision Analysis  1795.3.4 Game Theory  1825.3.5 Optimization  1865.4 Modeling Paradigms  192Case Studies  193Summary  199Key Terms  201Exercises  202x ContentsReferences  204Further Reading  2066 Business Process Modeling 207Rafael Diaz  Joshua G. Behr  and Mandar Tulpule6.1 Introduction and Learning Objectives  2076.2 Background  2076.3 Discrete-Event Simulation  2146.3.1 Introduction  2146.3.2 Fundamentals  2156.3.3 Queuing System Model Components  2186.3.4 Time Advance Mechanism  2196.3.5 Simulation Flowchart  2206.4 Discrete-Event Simulation Case Study  2216.4.1 Introduction  2226.4.2 Background  2226.4.3 Research Question  2236.4.4 Overview of Optimization Model  2246.4.5 The Simulation Model  2256.4.6 Experimental Setting  2256.4.7 Simulation Parameterization and Execution  2266.4.8 Weigh Zones and Product Reassignment  2266.4.9 Results  2266.5 System Dynamics Simulation  2276.5.1 Introduction  2276.5.2 Fundamentals  2286.5.3 The Stock and Flow Diagrams  2296.5.4 Model Calibration  2316.5.5 Model Testing  2336.5.6 Population Modeling Exercise  2336.5.7 Application of System Dynamics  2356.5.8 Background  2356.5.9 Research Question  2386.5.10 Dynamic Hypothesis  2386.5.11 Causal Loop Diagram  2386.5.12 Stock and Flow Model  2396.5.13 Simulation and Results  2406.5.14 Conclusions  2446.6 Monte Carlo Simulation  2446.6.1 Introduction  2446.6.2 Fundamentals  2456.6.3 Probability Theory and Monte Carlo  2476.6.4 Central Limit Theorem  2476.6.5 Three-Sigma Rule  2476.6.6 Monte Carlo Case Study  2496.6.7 Research Question  2506.6.8 Model Parameters  2506.6.9 Simulation Procedure  2506.6.10 Estimating Profit  2516.6.11 Excel Implementation  2536.6.12 Outcomes  2536.6.13 Conclusions  254Summary  255Key Terms  255Review Questions  256References  2577 A Review of Mesh Generation for Medical Simulators 261Michel A. Audette  Andrey N. Chernikov  and Nikos P. Chrisochoides7.1 Introduction and Learning Objectives  2617.2 Background—A Survey of Relevant Biomechanics and Open-Source Software  2637.2.1 Architecture of an Interactive Medical Simulator  2637.2.2 Mechanics of Tissue Manipulation in Medical Simulation  2647.2.3 Mechanics of Tissue Cutting and Resection in Medical Simulation  2697.2.4 Open-Source Resources in Medical Simulation  2697.3 Theory—The Impact of Element Quality and Size on Simulation  2727.4 Modeling Paradigms—Methods for Mesh Generation  2767.4.1 Structured Tetrahedral Mesh Generation  2767.4.2 Unstructured Tetrahedral Mesh Generation  2767.4.3 Octree-Based Unstructured Tetrahedral Mesh Generation  2797.4.4 Delaunay Unstructured Tetrahedral Mesh Generation  2807.4.5 Advancing Front Unstructured Tetrahedral Mesh Generation  2847.4.6 Optimization-Based Unstructured Tetrahedral Mesh Generation  2847.4.7 Unstructured Surface Mesh Generation  285Case Studies  289Summary  291Key Terms  292Acknowledgments  293Exercises  293References  2948 Military Interoperability Challenges 298Saikou Y. Diallo and Jos´e J. Padilla8.1 Introduction and Learning Objectives  2988.2 Background  2998.2.1 Overview  3008.2.2 State of the Art in Interoperability  3008.2.3 Levels of Interoperability  3028.2.4 Current Approaches to Interoperation  3038.3 Theory  3058.3.1 Data Models  3068.3.2 A Relational Model of Data in M&S Systems  307Case Study: Live Virtual Constructive Simulation Environments  3118.4 Live Virtual Constructive  3118.5 LVC Examples  3158.6 Distributed Simulation Engineering and Execution Process (DSEEP)  3168.7 LVC Architecture Framework (LVCAF)  3208.8 Simulation Systems  322Summary  323Key Terms  324Exercises  325References  325Index 329