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Informationen zum Autor John R. Clymer, PhD, is a Professor at California State University at Fullerton. Klappentext A hands-on approach to understanding, designing, analyzing, and evaluating complex systemsDuring the last few years, Simulation-Based Systems Engineering (SBSE) has become an essential tool for the design and evaluation of complex systems. This is the first book to cover the basic principles of complex systems through the use of hands-on experimentation using an icon-based simulation tool.Utilizing the accompanying software tool ExtendSim, which works with the OpEMCSS library, readers are invited to engage in simulation-basedexperiments that demonstrate the principles of complex systems with anemphasis on design, analysis, and evaluation. A number of real-world examples are included to demonstrate how to model complex systems across a range of engineering, business, societal, economic, and scientific disciplines.Beginning with an introduction to SBSE, the book covers:* Simulation concepts and building blocks* Systems design and model development* Markov model development* Reliability processes* Queuing theory in SBSE* Rule-based learning and adaptation* Agent motion and spatial interactions* Multi-agent system of systemsAssuming only a very basic background in problem-solving ability, this book is ideal as a textbook for students (a homework solution manual is also available) and as a reference book for practitioners in industry. Zusammenfassung The book includes real world examples of Complex Adaptive Systems (CAS), such as a traffic control network in order for the reader to gain a good understanding of the properties of CAS Focuses on Operational Evaluation Modeling (OpEM) where simulation of a system in its operational environment is a main theme. Inhaltsverzeichnis Preface xiii Acknowledgments xvii Overview xix 1 Introduction to Simulation-Based Systems Engineering 1 1.1 Definition of Complex Systems 3 1.1.1 Exercise: Model a Goal-Oriented Activity 6 1.1.2 Agent-Based System Architectures 9 1.1.3 Simulation and AI-Based System Design 11 1.1.4 Expansionism Versus Reductionism 12 1.1.5 Summary 15 1.2 Using Simulation to Understand Complex Systems 15 1.2.1 ExtendSim Discrete-Event Simulation User Environment and OpEMCSS Overview 15 1.2.2 Simulation Model Development Procedure 17 1.2.3 Simulation Programs: How Serial and Parallel Process Models Work 21 1.2.4 Sensitivity Analysis 29 1.3 Bringing Complex Systems into Being 30 1.3.1 Definition of Systems Engineering 31 1.3.2 Levels of System Description 33 1.3.3 Systems Engineering Life Cycle 35 1.3.4 Simulation of the System Development Process 38 1.3.5 Simulation-Based Systems Engineering 46 1.4 Summary 47 Problems 50 References 53 Bibliography 53 2 Simulation Concepts and Building Blocks 55 2.1 Statistical Aspects of Simulation 56 2.1.1 Convergence Theorems 57 2.1.2 Uniform Random-Number Generator 58 2.1.3 Discrete Probability Distributions 59 2.1.4 Goodness-of-Fit Test 60 2.1.5 Generation of Random Variables 62 2.2 OpEM Graphical Modeling Language 64 2.2.1 Petri Nets 65 2.2.2 OpEM Graphs 68 2.3 OpEM Parallel Process Simulations 72 2.3.1 Sequential Process Event 76 2.3.2 Split Event 78 2.3.3 Complex Assemble Event 80 2.3.4 Simple Assemble Event 83 2.3.5 Comparison of Petri Nets and OpEM Graphs 84 2.4 OpEMCSS Simulation of Context-Sensitive Systems 86 2.4.1 Types of CSS Process Interactions and Timeline Analysis 86 2.4.2 How ExtendSim Has Been Modified to Implement the OpEM Language 88 2.4.3 How OpEMCSS Blocks Work Together to Model an Example C...
