Discrete-Event System Simulation: Pearson New International Edition

Series
Pearson
Author
Jerry Banks / John S. Carson / Barry L. Nelson / David M. Nicol  
Publisher
Pearson
Cover
Softcover
Edition
5
Language
English
Total pages
564
Pub.-date
July 2013
ISBN13
9781292024370
ISBN
1292024372
Related Titles


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9781292024370
Discrete-Event System Simulation: Pearson New International Edition
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Description

For junior- and senior-level simulation courses in engineering, business, or computer science.

While most books on simulation focus on particular software tools, Discrete Event System Simulation examines the principles of modeling and analysis that translate to all such tools. This language-independent text explains the basic aspects of the technology, including the proper collection and analysis of data, the use of analytic techniques, verification and validation of models, and designing simulation experiments. It offers an up-to-date treatment of simulation of manufacturing and material handling systems, computer systems, and computer networks.

Students and instructors will find a variety of resources at the associated website, www.bcnn.net/, including simulation source code for download, additional exercises and solutions, web links and errata.

Features

  • Simulation of Communications Systems includes new material on simulation beta distribution, negative binomial distribution and non-stationary processes.
  • Subset selection methods used for output analysis of several alternatives are discussed.
  • The text’s Companion Website includes software downloads, links and Power Point Lecture Slides, and extends the text material.
  • Numerous solved examples enhance understanding of concepts.
  • Abundant figures, tables and end-chapter exercises are provided.
  • Application topics promote understanding of real-world uses.
  • Interpretation of simulation software output explains how to use software tools correctly.
  • Discussion of simple tools for complex input modeling problems develops more realistic valid models.
  • New to this Edition

  • New treatment of input modeling highlights difficulties and solutions for real applications.
  • Updated examples of simulation experiment design and analysis are provided.
  • More Excel examples and supplements enhance the material.
  • A more realistic presentation of input modeling in Chapter 9.
  • Emphasis on appropriate analysis within the confines of commercial products, and a supplementary Excel tool that adds to the capabilities of those products.
    — Addresses that some texts present advanced simulation analysis methods that cannot be used in practice if they are not included in the software the practitioner actually uses, or are not readily available as a supplementary tool.
  • Table of Contents

    I Introduction to Discrete-Event System Simulation 1

    Chapter 1 Introduction to Simulation 3

    1.1 When Simulation Is the Appropriate Tool 4

    1.2 When Simulation Is Not Appropriate 4

    1.3 Advantages and Disadvantages of Simulation 5

    1.4 Areas of Application 7

    1.5 Systems and System Environment 9

    1.6 Components of a System 9

    1.7 Discrete and Continuous Systems 11

    1.8 Model of a System 12

    1.9 Types of Models 13

    1.10 Discrete-Event System Simulation 13

    1.11 Steps in a Simulation Study 14

    References 18

    Exercises 19

     

    Chapter 2 Simulation Examples 21

    2.1 Simulation of Queueing Systems 22

    2.2 Simulation of Inventory Systems 39

    2.3 Other Examples of Simulation 46

    2.4 Summary 57

    References 57

    Exercises 57

     

    Chapter 3 General Principles 67

    3.1 Concepts in Discrete-Event Simulation 68

    3.1.1 The Event Scheduling/Time Advance Algorithm 71

    3.1.2 World Views 74

    3.1.3 Manual Simulation Using Event Scheduling 77

    3.2 List Processing 86

    3.2.1 Lists: Basic Properties and Operations 87

    3.2.2 Using Arrays for List Processing 88

    3.2.3 Using Dynamic Allocation and Linked Lists 90

    3.2.4 Advanced Techniques 92

    3.3 Summary 92

    References 92

    Exercises 93

     

    Chapter 4 Simulation Software 95

    4.1 History of Simulation Software 96

    4.1.1 The Period of Search (1955—60) 97

    4.1.2 The Advent (1961—65) 97

    4.1.3 The Formative Period (1966—70) 97

    4.1.4 The Expansion Period (1971—78) 98

    4.1.5 Consolidation and Regeneration (1979—86) 98

    4.1.6 Integrated Environments (1987—Present) 99

    4.2 Selection of Simulation Software 99

    4.3 An Example Simulation 102

    4.4 Simulation in Java 104

    4.5 Simulation in GPSS 112

    4.6 Simulation in SSF 117

    4.7 Simulation Software 120

    4.7.1 Arena 122

    4.7.2 AutoMod 123

    4.7.3 Extend 124

    4.7.4 Flexsim 124

    4.7.5 Micro Saint 125

    4.7.6 ProModel 125

    4.7.7 QUEST 126

    4.7.8 SIMUL8 127

    4.7.9 WITNESS 128

    4.8 Experimentation and Statistical-Analysis Tools 128

    4.8.1 Common Features 128

    4.8.2 Products 129

    References 131

    Exercises 132

     

    II Mathematical and Statistical Models 147

    Chapter 5 Statistical Models in Simulation 149

    5.1 Review of Terminology and Concepts 150

    5.2 Useful Statistical Models 156

    5.3 Discrete Distributions 160

    5.4 Continuous Distributions 166

    5.5 Poisson Process 186

    5.5.1 Properties of a Poisson Process 188

    5.5.2 Nonstationary Poisson Process 189

    5.6 Empirical Distributions 190

    5.7 Summary 193

    References 193

    Exercises 193

     

    Chapter 6 Queueing Models 201

    6.1 Characteristics of Queueing Systems 202

    6.1.1 The Calling Population 202

    6.1.2 System Capacity 204

    6.1.3 The Arrival Process 204

    6.1.4 Queue Behavior and Queue Discipline 205

    6.1.5 Service Times and the Service Mechanism 206

    6.2 Queueing Notation 208

    6.3 Long-Run Measures of Performance of Queueing Systems 208

    6.3.1 Time-Average Number in System L 209

    6.3.2 Average Time Spent in System Per Customer w 211

    6.3.3 The Conservation Equation: L = λw 212

    6.3.4 Server Utilization 213

    6.3.5 Costs in Queueing Problems 218

    6.4 Steady-State Behavior of Infinite-Population Markovian Models 220

    6.4.1 Single-Server Queues with Poisson Arrivals and Unlimited Capacity: M/G/1 221

    6.4.2 Multiserver Queue: M/M/c/∞/∞ 227

    6.4.3 Multiserver Queues with Poisson Arrivals and Limited Capacity: M/M/c/N/233

    6.5 Steady-State Behavior of Finite-Population Models (M/M/c/K/K) 235

    6.6 Networks of Queues 239

    6.7 Summary 241

    References 242

    Exercises 243

     

    III Random Numbers 249

    Chapter 7 Random-Number Generation 251

    7.1 Properties of Random Numbers 251

    7.2 Generation of Pseudo-Random Numbers 252

    7.3 Techniques for Generating Random Numbers 253

    7.3.1 Linear Congruential Method 254

    7.3.2 Combined Linear Congruential Generators 257

    7.3.3 Random-Number Streams 259

    7.4 Tests for Random Numbers 260

    7.4.1 Frequency Tests 261

    7.4.2 Tests for Autocorrelation 265

    7.5 Summary 267

    References 268

    Exercises 269

     

    Chapter 8 Random-Variate Generation 272

    8.1 Inverse-Transform Technique 273

    8.1.1 Exponential Distribution 273

    8.1.2 Uniform Distribution 276

    8.1.3 Weibull Distribution 277

    8.1.4 Triangular Distribution 278

    8.1.5 Empirical Continuous Distributions 279

    8.1.6 Continuous Distributions without a Closed-Form Inverse 283

    8.1.7 Discrete Distributions 284

    8.2 Acceptance—Rejection Technique 289

    8.2.1 Poisson Distribution 290

    8.2.2 Nonstationary Poisson Process 293

    8.2.3 Gamma Distribution 294

    8.3 Special Properties 296

    8.3.1 Direct Transformation for the Normal and Lognormal Distributions 296

    8.3.2 Convolution Method 298

    8.3.3 More Special Properties 299

    8.4 Summary 299

    References 299

    Exercises 300

     

    IV Analysis of Simulation Data 305

    Chapter 9 Input Modeling 307

    9.1 Data Collection 308

    9.2 Identifying the Distribution with Data 310

    9.2.1 Histograms 310

    9.2.2 Selecting the Family of Distributions 313

    9.2.3 Quantile—Quantile Plots 316

    9.3 Parameter Estimation 319

    9.3.1 Preliminary Statistics: Sample Mean and Sample Variance 319

    9.3.2 Suggested Estimators 321

    9.4 Goodness-of-Fit Tests 326

    9.4.1 Chi-Square Test 327

    9.4.2 Chi-Square Test with Equal Probabilities 329

    9.4.3 Kolmogorov—Smirnov Goodness-of-Fit Test 331

    9.4.4 p-Values and “Best Fits” 333

    9.5 Fitting a Nonstationary Poisson Process 334

    9.6 Selecting Input Models without Data 335

    9.7 Multivariate and Time-Series Input Models 337

    9.7.1 Covariance and Correlation 337

    9.7.2 Multivariate Input Models 338

    9.7.3 Time-Series Input Models 340

    9.7.4 The Normal-to-Anything Transformation 342

    9.8 Summary 344

    References 345

    Exercises 346

     

    Chapter 10 Verification and Validation of Simulation Models 354

    10.1 Model-Building, Verification, and Validation 355

    10.2 Verification of Simulation Models 356

    10.3 Calibration and Validation of Models 361

    10.3.1 Face Validity 362

    10.3.2 Validation of Model Assumptions 362

    10.3.3 Validating Input—Output Transformations 363

    10.3.4 Input—Output Validation: Using Historical Input Data 374

    10.3.5 Input—Output Validation: Using a Turing Test 378

    10.4 Summary 379

    References 379

    Exercises 381

     

    Chapter 11 Output Analysis for a Single Model 383

    11.1 Types of Simulations with Respect to Output Analysis 384

    11.2 Stochastic Nature of Output Data 387

    11.3 Measures of Performance and Their Estimation 390

    11.3.1 Point Estimation 390

    11.3.2 Confidence-Interval Estimation 392

    11.4 Output Analysis for Terminating Simulations 393

    11.4.1 Statistical Background 394

    11.4.2 Confidence Intervals with Specified Precision 397

    11.4.3 Quantiles 399

    11.4.4 Estimating Probabilities and Quantiles from Summary Data 400

    11.5 Output Analysis for Steady-State Simulations 402

    11.5.1 Initialization Bias in Steady-State Simulations 403

    11.5.2 Error Estimation for Steady-State Simulation 409

    11.5.3 Replication Method for Steady-State Simulations 413

    11.5.4 Sample Size in Steady-State Simulations 417

    11.5.5 Batch Means for Interval Estimation in Steady-State Simulations 418

    11.5.6 Quantiles 422

    11.6 Summary 423

    References 423

    Exercises 424

     

    Chapter 12 Comparison and Evaluation of Alternative System Designs 432

    12.1 Comparison of Two System Designs 433

    12.1.1 Independent Sampling with Equal Variances 436

    12.1.2 Independent Sampling with Unequal Variances 438

    12.1.3 Common Random Numbers (CRN) 438

    12.1.4 Confidence Intervals with Specified Precision 446

    12.2 Comparison of Several System Designs 448

    12.2.1 Bonferroni Approach to Multiple Comparisons 449

    12.2.2 Bonferroni Approach to Selecting the Best 454

    12.2.3 Bonferroni Approach to Screening 457

    12.3 Metamodeling 458

    12.3.1 Simple Linear Regression 459

    12.3.2 Testing for Significance of Regression 463

    12.3.3 Multiple Linear Regression 466

    12.3.4 Random-Number Assignment for Regression 466

    12.4 Optimization via Simulation 467

    12.4.1 What Does ‘Optimization via Simulation’ Mean? 468

    12.4.2 Why is Optimization via Simulation Difficult? 469

    12.4.3 Using Robust Heuristics 470

    12.4.4 An Illustration: Random Search 473

    12.5 Summary 476

    References 476

    Exercises 477

     

    V Applications 483

    Chapter 13 Simulation of Manufacturing and Material-Handling Systems 485

    13.1 Manufacturing and Material-Handling Simulations 486

    13.1.1 Models of Manufacturing Systems 486

    13.1.2 Models of Material-Handling 487

    13.1.3 Some Common Material-Handling Equipment 488

    13.2 Goals and Performance Measures 489

    13.3 Issues in Manufacturing and Material-Handling Simulations 490

    13.3.1 Modeling Downtimes and Failures 491

    13.3.2 Trace-Driven Models 495

    13.4 Case Studies of the Simulation of Manufacturing and Material-Handling Systems 496

    13.5 Manufacturing Example: A Job-Shop Simulation 499

    13.5.1 System Description and Model Assumptions 499

    13.5.2 Presimulation Analysis 502

    13.5.3 Simulation Model and Analysis of the Designed System 503

    13.5.4 Analysis of Station Utilization 503

    13.5.5 Analysis of Potential System Improvements 504

    13.5.6 Concluding Words 506

    13.6 Summary 506

    References 506

    Exercises 507

     

     

    Chapter 14 Simulation of Computer Networks 550

    15.1 Introduction 550

    15.2 Traffic Modeling 552

    15.3 Media Access Control 555

    15.3.1 Token-Passing Protocols 556

    15.3.2 Ethernet 559

    15.4 Data Link Layer 561

    15.5 TCP 562

    15.6 Model Construction 569

    15.6.1 Construction 569

    15.6.2 Example 571

    15.7 Summary 573

    References 574

    Exercises 574

    Appendix 576

    Index 591