Handbook of Real-World Applications in Modeling and Simulation

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.

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