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Copyright, ii
Preface, xi
What’s New in the Fifth Edition, xv
List of Materials Available on www.bcnn.net, xvi
About the Authors, xvii
Part I. Introduction to Discrete-Event System Simulation, 1
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. Some Recent Applications of Simulation, 9
1.6. Systems and System Environment, 12
1.7. Components of a System, 12
1.8. Discrete and Continuous Systems, 14
1.9. Model of a System, 15
1.10. Types of Models, 15
1.11. Discrete-Event System Simulation, 16
1.12. Steps in a Simulation Study, 16
References, 21
Exercises, 22
2. Simulation Examples in a Spreadsheet, 24
2.1. The Basics of Spreadsheet Simulation, 25
2.1.1. How to Simulate Randomness, 25
2.1.2. The Random Generators Used in the Examples, 27
2.1.3. How to Use the Spreadsheets, 28
2.1.4. How to Simulate a Coin Toss, 29
2.1.5. How to Simulate a Random Service Time, 30
2.1.6. How to Simulate a Random Arrival Time, 33
2.1.7. A Framework for Spreadsheet Simulation, 35
2.2. A Coin Tossing Game, 37
2.3. Queueing Simulation in a Spreadsheet, 40
2.3.1. Waiting Line Models, 41
2.3.2. Simulating a Single-Server Queue, 45
2.3.3. Simulating a Queue with Two Servers, 51
2.4. Inventory Simulation in a Spreadsheet, 55
2.4.1. Simulating the News Dealer’s Problem, 57
2.4.2. Simulating an (M, N) Inventory Policy, 61
2.5. Other Examples of Simulation, 65
2.5.1. Simulation of a Reliability Problem, 65
2.5.2. Simulation of Hitting a Target, 68
2.5.3. Estimating the Distribution of Lead-Time Demand, 71
2.5.4. Simulating an Activity Network, 73
2.6. Summary, 76
References, 77
Exercises, 78
3. General Principles, 88
3.1. Concepts in Discrete-Event Simulation, 89
3.1.1. The Event Scheduling/Time Advance Algorithm, 92
3.1.2. World Views, 96
3.1.3. Manual Simulation Using Event Scheduling, 98
3.2. List Processing, 108
3.2.1. Basic Properties and Operations Performed on Lists, 109
3.2.2. Using Arrays for List Processing, 110
3.2.3. Using Dynamic Allocation and Linked Lists, 112
3.2.4. Advanced Techniques, 114
3.3. Summary, 114
References, 115
Exercises, 115
4. Simulation Software, 117
4.1. History of Simulation Software, 118
4.1.1. The Period of Search (1955–1960), 119
4.1.2. The Advent (1961–1965), 119
4.1.3. The Formative Period (1966–1970), 119
4.1.4. The Expansion Period (1971–1978), 120
4.1.5. The Period of Consolidation and Regeneration (1979–1986), 120
4.1.6. The Period of Integrated Environments (1987–2008), 121
4.1.7. The Future (2009–2011), 121
4.2. Selection of Simulation Software, 122
4.3. An Example Simulation, 126
4.4. Simulation in Java, 126
4.5. Simulation in GPSS, 137
4.6. Simulation in SSF, 141
4.7. Simulation Environments, 145
4.7.1. AnyLogic, 146
4.7.2. Arena, 147
4.7.3. AutoMod, 148
4.7.4. Enterprise Dynamics, 149
4.7.5. ExtendSim, 149
4.7.6. Flexsim, 150
4.7.7. ProModel, 151
4.7.8. SIMUL8, 151
4.8. Experimentation and Statistical-Analysis Tools, 152
4.8.1. Common Features, 152
4.8.2. Products, 152
References, 155
Exercises, 156
Part II. Mathematical and Statistical Models, 169
5. Statistical Models in Simulation, 171
5.1. Review of Terminology and Concepts, 172
5.1.1. Discrete random variables, 172
5.1.2. Continuous random variables, 173
5.1.3. Cumulative distribution function, 175
5.1.4. Expectation, 177
5.1.5. The mode, 179
5.2. Useful Statistical Models, 179
5.2.1. Queueing systems, 179
5.2.2. Inventory and supply-chain systems, 182
5.2.3. Reliability and maintainability, 183
5.2.4. Limited data, 183
5.2.5. Other distributions, 183
5.3. Discrete Distributions, 183
5.3.1. Bernoulli trials and the Bernoulli distribution, 183
5.3.2. Binomial distribution, 184
5.3.3. Geometric and Negative Binomial distributions, 186
5.3.4. Poisson distribution, 187
5.4. Continuous Distributions, 189
5.4.1. Uniform distribution, 189
5.4.2. Exponential distribution, 191
5.4.3. Gamma distribution, 194
5.4.4. Erlang distribution, 195
5.4.5. Normal distribution, 197
5.4.6. Weibull distribution, 204
5.4.7. Triangular distribution, 206
5.4.8. Lognormal distribution, 209
5.4.9. Beta distribution, 210
5.5. Poisson Process, 211
5.5.1. Properties of a Poisson Process, 213
5.5.2. Nonstationary Poisson Process, 214
5.6. Empirical Distributions, 216
5.7. Summary, 218
References, 219
Exercises, 219
6. Queueing Models, 227
6.1. Characteristics of Queueing Systems, 228
6.1.1. The Calling Population, 229
6.1.2. System Capacity, 230
6.1.3. The Arrival Process, 230
6.1.4. Queue Behavior and Queue Discipline, 232
6.1.5. Service Times and the Service Mechanism, 232
6.2. Queueing Notation, 234
6.3. Long-Run Measures of Performance of Queueing Systems, 235
6.3.1. Time-Average Number in System L, 235
6.3.2. Average Time Spent in System Per Customer w, 237
6.3.3. The Conservation Equation: L = λ w, 239
6.3.4. Server Utilization, 240
6.3.5. Costs in Queueing Problems, 246
6.4. Steady-State Behavior of Infinite-Population Markovian Models, 247
6.4.1. Single-Server Queues with Poisson Arrivals and Unlimited Capacity: M/G/1, 248
6.4.2. Multiserver Queue: M/M/c/∞/∞, 253
6.4.3. Multiserver Queues with Poisson Arrivals and Limited Capacity: M/M/c/N/∞, 258
6.5. Steady-State Behavior of Finite-Population Models (M/M/c/K/K), 260
6.6. Networks of Queues, 263
6.7. Rough-cut Modeling: An Illustration, 265
6.8. Summary, 267
References, 268
Exercises, 268
Part III. Random Numbers, 275
7. Random-Number Generation, 277
7.1. Properties of Random Numbers, 277
7.2. Generation of Pseudo-Random Numbers, 278
7.3. Techniques for Generating Random Numbers, 279
7.3.1. Linear Congruential Method, 280
7.3.2. Combined Linear Congruential Generators, 283
7.3.3. Random-Number Streams, 285
7.4. Tests for Random Numbers, 286
7.4.1. Frequency Tests, 287
7.4.2. Tests for Autocorrelation, 291
7.5. Summary, 294
References, 294
Exercises, 295
8. Random-Variate Generation, 299
8.1. Inverse-Transform Technique, 300
8.1.1. Exponential Distribution, 300
8.1.2. Uniform Distribution, 303
8.1.3. Weibull Distribution, 305
8.1.4. Triangular Distribution, 305
8.1.5. Empirical Continuous Distributions, 306
8.1.6. Continuous Distributions without a Closed-Form Inverse, 311
8.1.7. Discrete Distributions, 312
8.2. Acceptance-Rejection Technique, 316
8.2.1. Poisson Distribution, 318
8.2.2. Nonstationary Poisson Process, 321
8.2.3. Gamma Distribution, 322
8.3. Special Properties, 323
8.3.1. Direct Transformation for the Normal and Lognormal Distributions, 324
8.3.2. Convolution Method, 325
8.3.3. More Special Properties, 327
8.4. Summary, 327
References, 327
Exercises, 328
Part IV. Analysis of Simulation Data, 333
9. Input Modeling, 335
9.1. Data Collection, 336
9.2. Identifying the Distribution with Data, 341
9.2.1. Histograms, 341
9.2.2. Selecting the Family of Distributions, 344
9.2.3. Quantile-Quantile Plots, 347
9.3. Parameter Estimation, 350
9.3.1. Preliminary Statistics: Sample Mean and Sample Variance, 350
9.3.2. Suggested Estimators, 352
9.4. Goodness-of-Fit Tests, 358
9.4.1. Chi-Square Test, 359
9.4.2. Chi-Square Test with Equal Probabilities, 361
9.4.3. Kolmogorov–Smirnov Goodness-of-Fit Test, 363
9.4.4. p-Values and “Best Fits”, 365
9.5. Fitting a Nonstationary Poisson Process, 366
9.6. Selecting Input Models without Data, 367
9.7. Multivariate and Time-Series Input Models, 369
9.7.1. Covariance and Correlation, 370
9.7.2. Multivariate Input Models, 370
9.7.3. Time-Series Input Models, 372
9.7.4. The Normal-to-Anything Transformation, 374
9.8. Summary, 376
References, 378
Exercises, 379
10. Verification, Calibration, and Validation of Simulation Models, 388
10.1. Model Building, Verification, and Validation, 389
10.2. Verification of Simulation Models, 390
10.3. Calibration and Validation of Models, 395
10.3.1. Face Validity, 396
10.3.2. Validation of Model Assumptions, 397
10.3.3. Validating Input-Output Transformations, 397
10.3.4. Input-Output Validation: Using Historical Input Data, 408
10.3.5. Input-Output Validation: Using a Turing Test, 412
10.4. Summary, 413
References, 414
Exercises, 415
11. Estimation of Absolute Performance, 417
11.1. Types of Simulations with Respect to Output Analysis, 418
11.2. Stochastic Nature of Output Data, 421
11.3. Absolute Measures of Performance and Their Estimation, 423
11.3.1. Point Estimation, 423
11.3.2. Confidence-Interval Estimation, 425
11.4. Output Analysis for Terminating Simulations, 427
11.4.1. Statistical Background, 427
11.4.2. Confidence Intervals with Specified Precision, 431
11.4.3. Quantiles, 433
11.4.4. Estimating Probabilities and Quantiles from Summary Data, 434
11.5. Output Analysis for Steady-State Simulations, 435
11.5.1. Initialization Bias in Steady-State Simulations, 436
11.5.2. Error Estimation for Steady-State Simulation, 440
11.5.3. Replication Method for Steady-State Simulations, 444
11.5.4. Sample Size in Steady-State Simulations, 448
11.5.5. Batch Means Method for Steady-State Simulations, 449
11.5.6. Steady-State Quantiles, 452
11.6. Summary, 453
References, 454
Exercises, 455
12. Estimation of Relative Performance, 463
12.1. Comparison of Two System Designs, 464
12.1.1. Independent Sampling, 467
12.1.2. Common Random Numbers (CRN), 468
12.1.3. Confidence Intervals with Specified Precision, 474
12.2. Comparison of Several System Designs, 475
12.2.1. Bonferroni Approach to Multiple Comparisons, 476
12.2.2. Selection of the Best, 478
12.3. Metamodeling, 483
12.3.1. Simple Linear Regression, 483
12.3.2. Metamodeling and Computer Simulation, 489
12.4. Optimization via Simulation, 491
12.4.1. What Does “Optimization via Simulation” Mean?, 492
12.4.2. Why is Optimization via Simulation Difficult?, 494
12.4.3. Using Robust Heuristics, 495
12.4.4. An Illustration: Random Search, 498
12.5. Summary, 500
References, 501
Exercises, 502
Part V. Applications, 507
13. Simulation of Manufacturing and Material-Handling Systems, 509
13.1. Manufacturing and Material-Handling Simulations, 510
13.1.1. Models of Manufacturing Systems, 510
13.1.2. Models of Material Handling Systems, 512
13.1.3. Some Common Material-Handling Equipment, 512
13.2. Goals and Performance Measures, 514
13.3. Issues in Manufacturing and Material-Handling Simulations, 515
13.3.1. Modeling Downtimes and Failures, 515
13.3.2. Trace-Driven Models, 519
13.4. Case Studies of the Simulation of Manufacturing and Material Handling, 520
13.5. Manufacturing Example: An Assembly-line Simulation, 523
13.5.1. System Description and Model Assumptions, 523
13.5.2. Presimulation Analysis, 526
13.5.3. Simulation Model and Analysis of the Designed System, 527
13.5.4. Analysis of Station Utilization, 527
13.5.5. Analysis of Potential System Improvements, 528
13.5.6. Concluding Words: The Gizmo Assembly-Line Simulation, 530
13.6. Summary, 530
References, 530
Exercises, 531
14. Simulation of Networked Computer Systems, 541
14.1. Introduction, 542
14.2. Simulation Tools, 544
14.2.1. Process Orientation, 546
14.2.2. Event Orientation, 548
14.3. Model Input, 549
14.3.1. Modulated Poisson Process, 550
14.3.2. Poisson-Pareto Process, 551
14.3.3. Pareto-length Phase Time, 555
14.3.4. WWW Traffic, 557
14.4. Mobility Models in Wireless Systems, 558
14.5. The OSI Stack Model, 560
14.6. Physical Layer in Wireless Systems, 562
14.6.1. Propagation Models, 562
14.6.2. Determining the Receivers, 568
14.7. Media Access Control, 569
14.7.1. Token-Passing Protocols, 569
14.7.2. Ethernet, 573
14.8. Data Link Layer, 575
14.9. TCP, 576
14.10. Model Construction, 583
14.10.1. Construction, 583
14.10.2. DML Example, 584
14.11. Summary, 587
References, 588
Exercises, 589
Appendix, 591
Index, 607

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