Introduces various modeling and simulation methods and paradigms that are used to explain and solve the predominant challenges facing society Handbook of Real-World Applications in Modeling and Simulation provides a thorough explanation of modeling and simulation in the most useful, current, and predominant applied areas of transportation, homeland security, medicine, operational research, military science, and business modeling. Offering a cutting-edge and accessible presentation, this book discusses how and why the presented domains have become leading applications of modeling and simulation techniques. Contributions from leading academics and researchers integrate modeling and simulation theories, methods, and data to analyze challenges that involve technological and social issues. The book begins with an introduction that explains why modeling and simulation is a reliable analysis assessment tool for complex systems problems. Subsequent chapters provide an orientation to various modeling and simulation methods and paradigms that are used to explain and solve the predominant challenges across real-world applied domains. Additionally, the handbook: Provides a practical one-stop reference on modeling and simulation and contains an accessible introduction to key concepts and techniques Introduces, trains, and prepares readers from statistics, mathematics, engineering, computer science, economics, and business to use modeling and simulation in their studies and research Features case studies that are representative of fundamental areas of multidisciplinary studies and provides a concise look at the key concepts of modeling and simulation Contains a collection of original ideas on modeling and simulation to help academics and practitioners develop a multifunctional perspective Self-contained chapters offer a comprehensive approach to explaining each respective domain and include sections that explore the related history, theory, modeling paradigms, and case studies. Key terms and techniques are clearly outlined, and exercise sets allow readers to test their comprehension of the presented material. Handbook of Real-World Applications in Modeling and Simulation is an essential reference for academics and practitioners in the areas of operations research, business, management science, engineering, statistics, mathematics, and computer science. The handbook is also a suitable supplement for courses on modeling and simulation at the graduate level.

Table of Contents:
Contributors xiii Preface xvii Introduction 1 1 Research and Analysis for Real-World Applications 8 Catherine M. Banks 1.1 Introduction and Learning Objectives  8 1.1.1 Learning Objectives  10 1.2 Background  10 1.3 M&S Theory and Toolbox  13 1.3.1 Simulation Paradigms  15 1.3.2 Types of Modeling  16 1.3.3 Modeling Applications  17 1.4 Research and Analysis Methodologies  18 Case Study: A Methodology for M&S Project Progression  20 Summary  23 Key Terms  24 Exercises  25 References  25 2 Human Behavior Modeling: A Real-World Application 26 John A. Sokolowski 2.1 Introduction and Learning Objectives  26 2.2 Background and Theory  27 2.2.1 Classical Decision Theory  27 2.2.2 Naturalistic Decision Making  31 2.2.3 Recognition-Primed Decision Model  33 2.2.4 Military Decision Making  37 2.2.5 Computational Techniques for Implementing the CJTF Decision Process  40 2.2.6 Summary of the State-of-the-Art  53 Case Studies  54 Summary  81 Key Terms  82 Exercises  83 References  83 Appendix: A Decision Scenario and Associated Data  88 3 Transportation 93 R. Michael Robinson 3.1 Introduction and Learning Objectives  93 3.2 Background  94 3.3 Theory  95 3.3.1 Simulation Levels  95 3.3.2 Traffic Analysis Zones  97 3.3.3 The Four-Step Model  98 3.3.4 Method of Successive Averages  102 3.3.5 Volume Delay Functions  105 3.3.6 Dynamic Traffic Assignment  108 3.4 Transportation Modeling Applications  113 3.4.1 Traffic Demand Models  113 3.4.2 Public Transportation Models  114 3.4.3 Freight Modeling  117 3.4.4 Evacuation Simulations  121 Summary  124 Key Terms  125 Exercises  126 References  126 Further Reading  127 4 Homeland Security Risk Modeling 129 Barry C. Ezell 4.1 Introduction and Learning Objectives  129 4.2 Background  131 4.2.1 Bioterrorism Risk Assessment 2006  132 4.2.2 Estimating Likelihood of Terrorist Events  133 4.2.3 Risk Assessed as a Function of Threat  Vulnerability  and Consequence  135 4.3 Theory and Applications in Risk Modeling  136 4.3.1 Philosophical Considerations  137 4.3.2 Ontology and Epistemology  138 4.3.3 Issues and Implications for the Risk Analyst  138 4.3.4 Philosophical Considerations Summary  141 4.3.5 System Principals and Applications for the Risk Analyst  142 4.3.6 Factors in Developing a Risk Assessment Study Plan  143 4.3.7 Scope and Bound in a Risk Study: Constraints  Limitations  and Assumptions  145 4.3.8 Well-Known Challenge in Homeland Security Studies  146 4.4 Elements of a Study Plan  147 4.5 Modeling Paradigms  148 4.5.1 Simple Verses Complex Methodologies  148 4.5.2 Quantitative and Qualitative Designs  148 4.5.3 Modeling Approaches and Examples  150 4.5.4 Verification and Validation for Risk Models  156 Case Studies  157 Summary  161 Key Terms  161 Exercises  161 References  162 Further Reading  164 5 Operations Research 165 Andrew J. Collins and Christine S.M. Currie 5.1 Introduction and Learning Objectives  165 5.2 Background  166 5.2.1 OR Techniques  168 5.3 Theory  169 5.3.1 Problem Structuring Methods  169 5.3.2 Queuing Theory  175 5.3.3 Decision Analysis  179 5.3.4 Game Theory  182 5.3.5 Optimization  186 5.4 Modeling Paradigms  192 Case Studies  193 Summary  199 Key Terms  201 Exercises  202 x Contents References  204 Further Reading  206 6 Business Process Modeling 207 Rafael Diaz  Joshua G. Behr  and Mandar Tulpule 6.1 Introduction and Learning Objectives  207 6.2 Background  207 6.3 Discrete-Event Simulation  214 6.3.1 Introduction  214 6.3.2 Fundamentals  215 6.3.3 Queuing System Model Components  218 6.3.4 Time Advance Mechanism  219 6.3.5 Simulation Flowchart  220 6.4 Discrete-Event Simulation Case Study  221 6.4.1 Introduction  222 6.4.2 Background  222 6.4.3 Research Question  223 6.4.4 Overview of Optimization Model  224 6.4.5 The Simulation Model  225 6.4.6 Experimental Setting  225 6.4.7 Simulation Parameterization and Execution  226 6.4.8 Weigh Zones and Product Reassignment  226 6.4.9 Results  226 6.5 System Dynamics Simulation  227 6.5.1 Introduction  227 6.5.2 Fundamentals  228 6.5.3 The Stock and Flow Diagrams  229 6.5.4 Model Calibration  231 6.5.5 Model Testing  233 6.5.6 Population Modeling Exercise  233 6.5.7 Application of System Dynamics  235 6.5.8 Background  235 6.5.9 Research Question  238 6.5.10 Dynamic Hypothesis  238 6.5.11 Causal Loop Diagram  238 6.5.12 Stock and Flow Model  239 6.5.13 Simulation and Results  240 6.5.14 Conclusions  244 6.6 Monte Carlo Simulation  244 6.6.1 Introduction  244 6.6.2 Fundamentals  245 6.6.3 Probability Theory and Monte Carlo  247 6.6.4 Central Limit Theorem  247 6.6.5 Three-Sigma Rule  247 6.6.6 Monte Carlo Case Study  249 6.6.7 Research Question  250 6.6.8 Model Parameters  250 6.6.9 Simulation Procedure  250 6.6.10 Estimating Profit  251 6.6.11 Excel Implementation  253 6.6.12 Outcomes  253 6.6.13 Conclusions  254 Summary  255 Key Terms  255 Review Questions  256 References  257 7 A Review of Mesh Generation for Medical Simulators 261 Michel A. Audette  Andrey N. Chernikov  and Nikos P. Chrisochoides 7.1 Introduction and Learning Objectives  261 7.2 Background—A Survey of Relevant Biomechanics and Open-Source Software  263 7.2.1 Architecture of an Interactive Medical Simulator  263 7.2.2 Mechanics of Tissue Manipulation in Medical Simulation  264 7.2.3 Mechanics of Tissue Cutting and Resection in Medical Simulation  269 7.2.4 Open-Source Resources in Medical Simulation  269 7.3 Theory—The Impact of Element Quality and Size on Simulation  272 7.4 Modeling Paradigms—Methods for Mesh Generation  276 7.4.1 Structured Tetrahedral Mesh Generation  276 7.4.2 Unstructured Tetrahedral Mesh Generation  276 7.4.3 Octree-Based Unstructured Tetrahedral Mesh Generation  279 7.4.4 Delaunay Unstructured Tetrahedral Mesh Generation  280 7.4.5 Advancing Front Unstructured Tetrahedral Mesh Generation  284 7.4.6 Optimization-Based Unstructured Tetrahedral Mesh Generation  284 7.4.7 Unstructured Surface Mesh Generation  285 Case Studies  289 Summary  291 Key Terms  292 Acknowledgments  293 Exercises  293 References  294 8 Military Interoperability Challenges 298 Saikou Y. Diallo and Jos´e J. Padilla 8.1 Introduction and Learning Objectives  298 8.2 Background  299 8.2.1 Overview  300 8.2.2 State of the Art in Interoperability  300 8.2.3 Levels of Interoperability  302 8.2.4 Current Approaches to Interoperation  303 8.3 Theory  305 8.3.1 Data Models  306 8.3.2 A Relational Model of Data in M&S Systems  307 Case Study: Live Virtual Constructive Simulation Environments  311 8.4 Live Virtual Constructive  311 8.5 LVC Examples  315 8.6 Distributed Simulation Engineering and Execution Process (DSEEP)  316 8.7 LVC Architecture Framework (LVCAF)  320 8.8 Simulation Systems  322 Summary  323 Key Terms  324 Exercises  325 References  325 Index 329