离散事件系统仿真 第4版 英文【2025|PDF|Epub|mobi|kindle电子书版本百度云盘下载】

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Ⅰ Introduction to Discrete-Event System Simulation1

Chapter 1 Introduction to Simulation3

1.1 When Simulation Is the Appropriate Tool4

1.2 When Simulation Is Not Appropriate4

1.3 Advantages and Disadvantages of Simulation5

1.4 Areas of Application7

1.5 Systems and System Environment9

1.6 Components of a System9

1.7 Discrete and Continuous Systems11

1.8 Model of a System12

1.9 Types of Models13

1.10 Discrete-Event System Simulation13

1.11 Steps in a Simulation Study14

References18

Exercises19

Chapter 2 Simulation Examples21

2.1 Simulation of Queueing Systems22

2.2 Simulation of Inventory Systems39

2.3 Other Examples of Simulation46

2.4 Summary57

References57

Exercises57

Chapter 3 General Principles67

3.1 Concepts in Discrete-Event Simulation68

3.1.1 The Event Scheduling/Time Advance Algorithm71

3.1.2 World Views74

3.1.3 Manual Simulation Using Event Scheduling77

3.2 List Processing86

3.2.1 Lists： Basic Properties and Operations87

3.2.2 Using Arrays for List Processing88

3.2.3 Using Dynamic Allocation and Linked Lists90

3.2.4 Advanced Techniques92

3.3 Summary92

References92

Exercises93

Chapter 4 Simulation Software95

4.1 History of Simulation Software96

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 Software99

4.3 An Example Simulation102

4.4 Simulation in Java104

4.5 Simulation in GPSS112

4.6 Simulation in SSF117

4.7 Simulation Software120

4.7.1 Arena122

4.7.2 AutoMod123

4.7.3 Extend124

4.7.4 Flexsim124

4.7.5 Micro Saint125

4.7.6 ProModel125

4.7.7 QUEST126

4.7.8 SIMUL8127

4.7.9 WITNESS128

4.8 Experimentation and Statistical-Analysis Tools128

4.8.1 Common Features128

4.8.2 Products129

References131

Exercises132

Ⅱ Mathematical and Statistical Models147

Chapter 5 Statistical Models in Simulation149

5.1 Review of Terminology and Concepts150

5.2 Useful Statistical Models156

5.3 Discrete Distributions160

5.4 Continuous Distributions166

5.5 Poisson Process186

5.5.1 Properties of a Poisson Process188

5.5.2 Nonstationary Poisson Process189

5.6 Empirical Distributions190

5.7 Summary193

References193

Exercises193

Chapter 6 Queueing Models201

6.1 Characteristics of Queueing Systems202

6.1.1 The Calling Population202

6.1.2 System Capacity204

6.1.3 The Arrival Process204

6.1.4 Queue Behavior and Queue Discipline205

6.1.5 Service Times and the Service Mechanism206

6.2 Queueing Notation208

6.3 Long-Run Measures of Performance of Queueing Systems208

6.3.1 Time-Average Number in System L209

6.3.2 Average Time Spent in System Per Customer w211

6.3.3 The Conservation Equation： L=λw212

6.3.4 Server Utilization213

6.3.5 Costs in Queueing Problems218

6.4 Steady-State Behavior of Infinite-Population Markovian Models220

6.4.1 Single-Server Queues with Poisson Arrivals and Unlimited Capacity： M/G/1221

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 Queues239

6.7 Summary241

References242

Exercises243

Ⅲ Random Numbers249

Chapter 7 Random-Number Generation251

7.1 Properties of Random Numbers251

7.2 Generation of Pseudo-Random Numbers252

7.3 Techniques for Generating Random Numbers253

7.3.1 Linear Congruential Method254

7.3.2 Combined Linear Congruential Generators257

7.3.3 Random-Number Streams259

7.4 Tests for Random Numbers260

7.4.1 Frequency Tests261

7.4.2 Tests for Autocorrelation265

7.5 Summary267

References268

Exercises269

Chapter 8 Random-Variate Generation272

8.1 Inverse-Transform Technique273

8.1.1 Exponential Distribution273

8.1.2 Uniform Distribution276

8.1.3 Weibull Distribution277

8.1.4 Triangular Distribution278

8.1.5 Empirical Continuous Distributions279

8.1.6 Continuous Distributions without a Closed-Form Inverse283

8.1.7 Discrete Distributions284

8.2 Acceptance-Rejection Technique289

8.2.1 Poisson Distribution290

8.2.2 Nonstationary Poisson Process293

8.2.3 Gamma Distribution294

8.3 Special Properties296

8.3.1 Direct Transformation for the Normal and Lognormal Distributions296

8.3.2 Convolution Method298

8.3.3 More Special Properties299

8.4 Summary299

References299

Exercises300

Ⅳ Analysis of Simulation Data305

Chapter 9 Input Modeling307

9.1 Data Collection308

9.2 Identifying the Distribution with Data310

9.2.1 Histograms310

9.2.2 Selecting the Family of Distributions313

9.2.3 Quantile-Quantile Plots316

9.3 Parameter Estimation319

9.3.1 Preliminary Statistics： Sample Mean and Sample Variance319

9.3.2 Suggested Estimators321

9.4 Goodness-of-Fit Tests326

9.4.1 Chi-Square Test327

9.4.2 Chi-Square Test with Equal Probabilities329

9.4.3 Kolmogorov-Smirnov Goodness-of-Fit Test331

9.4.4 p-Values and “Best Fits”333

9.5 Fitting a Nonstationary Poisson Process334

9.6 Selecting Input Models without Data335

9.7 Multivariate and Time-Series Input Models337

9.7.1 Covariance and Correlation337

9.7.2 Multivariate Input Models338

9.7.3 Time-Series Input Models340

9.7.4 The Normal-to-Anything Transformation342

9.8 Summary344

References345

Exercises346

Chapter 10 Verification and Validation of Simulation Models354

10.1 Model-Building， Verification， and Validation355

10.2 Verification of Simulation Models356

10.3 Calibration and Validation of Models361

10.3.1 Face Validity362

10.3.2 Validation of Model Assumptions362

10.3.3 Validating Input-Output Transformations363

10.3.4 Input-Output Validation： Using Historical Input Data374

10.3.5 Input-Output Validation： Using a Turing Test378

10.4 Summary379

References379

Exercises381

Chapter 11 Output Analysis for a Single Model383

11.1 Types of Simulations with Respect to Output Analysis384

11.2 Stochastic Nature of Output Data387

11.3 Measures of Performance and Their Estimation390

11.3.1 Point Estimation390

11.3.2 Confidence-Interval Estimation392

11.4 Output Analysis for Terminating Simulations393

11.4.1 Statistical Background394

11.4.2 Confidence Intervals with Specified Precision397

11.4.3 Quantiles399

11.4.4 Estimating Probabilities and Quantiles from Summary Data400

11.5 Output Analysis for Steady-State Simulations402

11.5.1 Initialization Bias in Steady-State Simulations403

11.5.2 Error Estimation for Steady-State Simulation409

11.5.3 Replication Method for Steady-State Simulations413

11.5.4 Sample Size in Steady-State Simulations417

11.5.5 Batch Means for Interval Estimation in Steady-State Simulations418

11.5.6 Quantiles422

11.6 Summary423

References423

Exercises424

Chapter 12 Comparison and Evaluation of Alternative System Designs432

12.1 Comparison of Two System Designs433

12.1.1 Independent Sampling with Equal Variances436

12.1.2 Independent Sampling with Unequal Variances438

12.1.3 Common Random Numbers （CRN）438

12.1.4 Confidence Intervals with Specified Precision446

12.2 Comparison of Several System Designs448

12.2.1 Bonferroni Approach to Multiple Comparisons449

12.2.2 Bonferroni Approach to Selecting the Best454

12.2.3 Bonferroni Approach to Screening457

12.3 Metamodeling458

12.3.1 Simple Linear Regression459

12.3.2 Testing for Significance of Regression463

12.3.3 Multiple Linear Regression466

12.3.4 Random-Number Assignment for Regression466

12.4 Optimization via Simulation467

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 Heuristics470

12.4.4 An Illustration： Random Search473

12.5 Summary476

References476

Exercises477

Ⅴ Applications483

Chapter 13 Simulation of Manufacturing and Material-Handling Systems485

13.1 Manufacturing and Material-Handling Simulations486

13.1.1 Models of Manufacturing Systems486

13.1.2 Models of Material-Handling487

13.1.3 Some Common Material-Handling Equipment488

13.2 Goals and Performance Measures489

13.3 Issues in Manufacturing and Material-Handling Simulations490

13.3.1 Modeling Downtimes and Failures491

13.3.2 Trace-Driven Models495

13.4 Case Studies of the Simulation of Manufacturing and Material-Handling Systems496

13.5 Manufacturing Example： A Job-Shop Simulation499

13.5.1 System Description and Model Assumptions499

13.5.2 Presimulation Analysis502

13.5.3 Simulation Model and Analysis of the Designed System503

13.5.4 Analysis of Station Utilization503

13.5.5 Analysis of Potential System Improvements504

13.5.6 Concluding Words506

13.6 Summary506

References506

Exercises507

Chapter 14 Simulation of Computer Systems517

14.1 Introduction517

14.2 Simulation Tools520

14.2.1 Process Orientation522

14.2.2 Event Orientation524

14.3 Model Input525

14.3.1 Modulated Poisson Process526

14.3.2 Virtual-Memory Referencing528

14.4 High-Level Computer-System Simulation534

14.5 CPU Simulation538

14.6 Memory Simulation543

14.7 Summary546

References546

Exercises547

Chapter 15 Simulation of Computer Networks550

15.1 Introduction550

15.2 Traffic Modeling552

15.3 Media Access Control555

15.3.1 Token-Passing Protocols556

15.3.2 Ethernet559

15.4 Data Link Layer561

15.5 TCP562

15.6 Model Construction569

15.6.1 Construction569

15.6.2 Example571

15.7 Summary573

References574

Exercises574

Appendix576

Index591