Item Details

Discrete-Event System Simulation

Jerry Banks ... [et al.]
Format
Book
Published
Upper Saddle River, NJ : Prentice Hall, 2001.
Edition
3rd ed
Language
English
Series
Prentice-Hall International Series in Industrial and Systems Engineering
ISBN
0130887021
Contents
  • 1.1 When Simulation Is the Appropriate Tool 4
  • 1.2 When Simulation Is Not Appropriate 5
  • 1.3 Advantages and Disadvantages of Simulation 6
  • 1.4 Areas of Application 7
  • 1.5 Systems and System Environment 9
  • 1.6 Components of a System 10
  • 1.7 Discrete and Continuous Systems 12
  • 1.8 Model of a System 13
  • 1.9 Types of Models 13
  • 1.10 Discrete-Event System Simulation 14
  • 1.11 Steps in a Simulation Study 15
  • 2 Simulation Examples 23
  • 2.1 Simulation of Queueing Systems 24
  • 2.2 Simulation of Inventory Systems 41
  • 2.3 Other Examples of Simulation 47
  • 3.1 Concepts in Discrete-Event Simulation 64
  • 3.1.1 Event-Scheduling/Time-Advance Algorithm 67
  • 3.1.2 World Views 72
  • 3.1.3 Manual Simulation Using Event Scheduling 75
  • 3.2 List Processing 85
  • 3.2.1 Lists: Basic Properties and Operations 86
  • 3.2.2 Using Arrays for List Processing 87
  • 3.2.3 Using Dynamic Allocation and Linked Lists 90
  • 3.2.4 Advanced Techniques 92
  • 4 Simulation Software 95
  • 4.1 History of Simulation Software 96
  • 4.1.1 Period of Search (1955-60) 97
  • 4.1.2 Advent (1961-65) 97
  • 4.1.3 Formative Period (1966-70) 98
  • 4.1.4 Expansion Period (1971-78) 98
  • 4.1.5 Consolidation and Regeneration (1979-86) 99
  • 4.1.6 Present Period (1987-present) 99
  • 4.2 Selection of Simulation Software 100
  • 4.3 An Example Simulation 104
  • 4.4 Simulation in C++ 104
  • 4.5 Simulation in GPSS 114
  • 4.6 Simulation in CSIM 119
  • 4.7 Simulation Packages 123
  • 4.7.1 Arena 123
  • 4.7.2 AutoMod 124
  • 4.7.3 Deneb/QUEST 125
  • 4.7.4 Extend 126
  • 4.7.5 Micro Saint 127
  • 4.7.6 ProModel 127
  • 4.7.7 Taylor ED 128
  • 4.7.8 WITNESS 128
  • 4.8 Experimentation and Statistical Analysis Tools 129
  • 4.8.1 Common Features 129
  • 4.8.2 Analysis Tools 129
  • 4.9 Trends in Simulation Software 131
  • 4.9.1 High-Fidelity Simulation 131
  • 4.9.2 Data Exchange Standards 132
  • 4.9.3 Internet 132
  • 4.9.4 Old Paradigm versus New Paradigm 133
  • 4.9.5 Component Libraries 133
  • 4.9.6 Distributed Manufacturing Simulation/High Level Architecture 133
  • 4.9.7 Embedded Simulation 134
  • 4.9.8 Optimization 134
  • Part 2 Mathematical and Statistical Models
  • 5 Statistical Models In Simulation 153
  • 5.2 Useful Statistical Models 160
  • 5.3 Discrete Distributions 165
  • 5.4 Continuous Distributions 170
  • 5.5 Poisson Process 190
  • 5.6 Empirical Distributions 193
  • 6 Queueing Models 204
  • 6.1 Characteristics of Queueing Systems 205
  • 6.1.1 Calling Population 206
  • 6.1.2 System Capacity 207
  • 6.1.3 Arrival Process 207
  • 6.1.4 Queue Behavior and Queue Discipline 209
  • 6.1.5 Service Times and the Service Mechanism 209
  • 6.2 Queueing Notation 211
  • 6.3 Long-Run Measures of Performance of Queueing Systems 212
  • 6.3.1 Time-Average Number in System L 213
  • 6.3.2 Average Time Spent in System per Customer, w 215
  • 6.3.3 Conservation Equation: L = [lambad]w 216
  • 6.3.4 Server Utilization 218
  • 6.3.5 Costs in Queueing Problems 223
  • 6.4 Steady-State Behavior of Infinite-Population Markovian Models 224
  • 6.4.1 Single-Server Queues with Poisson Arrivals and Unlimited Capacity: M/G/1 225
  • 6.4.2 Multiserver Queue: M/M/c/[infinity]/[infinity] 231
  • 6.4.3 Multiserver Queues with Poisson Arrivals and Limited Capacity: M/M/c/N/[infinity] 237
  • 6.5 Steady-State Behavior of Finite-Population Models (M/M/c/K/K) 239
  • 6.6 Networks of Queues 243
  • Part 3 Random Numbers
  • 7 Random-Number Generation 255
  • 7.1 Properties of Random Numbers 255
  • 7.2 Generation of Pseudo-Random Numbers 256
  • 7.3 Techniques for Generating Random Numbers 258
  • 7.3.1 Linear Congruential Method 258
  • 7.3.2 Combined Linear Congruential Generators 262
  • 7.4 Tests for Random Numbers 264
  • 7.4.1 Frequency Tests 266
  • 7.4.2 Runs Tests 270
  • 7.4.3 Tests for Autocorrelation 278
  • 7.4.4 Gap Test 281
  • 7.4.5 Poker Test 283
  • 8 Random-Variate Generation 289
  • 8.1 Inverse Transform Technique 290
  • 8.1.1 Exponential Distribution 290
  • 8.1.2 Uniform Distribution 294
  • 8.1.3 Weibull Distribution 294
  • 8.1.4 Triangular Distribution 295
  • 8.1.5 Empirical Continuous Distributions 296
  • 8.1.6 Continuous Distributions without a Closed-Form Inverse 300
  • 8.1.7 Discrete Distributions 301
  • 8.2 Direct Transformation for the Normal and Lognormal Distributions 307
  • 8.3 Convolution Method 309
  • 8.3.1 Erlang Distribution 309
  • 8.4 Acceptance-Rejection Technique 310
  • 8.4.1 Poisson Distribution 311
  • 8.4.2 Gamma Distribution 314
  • Part 4 Analysis of Simulation Data
  • 9 Input Modeling 323
  • 9.1 Data Collection 324
  • 9.2 Identifying the Distribution with Data 327
  • 9.2.1 Histograms 327
  • 9.2.2 Selecting the Family of Distributions 331
  • 9.2.3 Quantile-Quantile Plots 333
  • 9.3 Parameter Estimation 336
  • 9.3.1 Preliminary Statistics: Sample Mean and Sample Variance 336
  • 9.3.2 Suggested Estimators 338
  • 9.4 Goodness-of-Fit Tests 343
  • 9.4.1 Chi-Square Test 343
  • 9.4.2 Chi-Square Test with Equal Probabilities 346
  • 9.4.3 Kolmogorov-Smirnov Goodness-of-Fit Test 348
  • 9.4.4 p-Values and "Best Fits" 350
  • 9.5 Selecting Input Models without Data 351
  • 9.6 Multivariate and Time-Series Input Models 353
  • 9.6.1 Covariance and Correlation 354
  • 9.6.2 Multivariate Input Models 354
  • 9.6.3 Time-Series Input Models 356
  • 10 Verification and Validation of Simulation Models 367
  • 10.1 Model Building, Verification, and Validation 368
  • 10.2 Verification of Simulation Models 369
  • 10.3 Calibration and Validation of Models 374
  • 10.3.1 Face Validity 376
  • 10.3.2 Validation of Model Assumptions 377
  • 10.3.3 Validating Input-Output Transformations 377
  • 10.3.4 Input-Output Validation: Using Historical Input Data 388
  • 10.3.5 Input-Ouput Validation: Using a Turing Test 392
  • 11 Output Analysis for a Single Model 398
  • 11.1 Types of Simulations with Respect to Output Analysis 399
  • 11.2 Stochastic Nature of Output Data 402
  • 11.3 Measures of Performance and Their Estimation 407
  • 11.3.1 Point Estimation 407
  • 11.3.2 Interval Estimation 409
  • 11.4 Output Analysis for Terminating Simulations 410
  • 11.4.1 Statistical Background 410
  • 11.4.2 Confidence-Interval Estimation for a Fixed Number of Replications 411
  • 11.4.3 Confidence Intervals with Specified Precision 414
  • 11.4.4 Confidence Intervals for Quantiles 416
  • 11.5 Output Analysis for Steady-State Simulations 418
  • 11.5.1 Initialization Bias in Steady-State Simulations 419
  • 11.5.2 Statistical Background 426
  • 11.5.3 Replication Method for Steady-State Simulations 430
  • 11.5.4 Sample Size in Steady-State Simulations 434
  • 11.5.5 Batch Means for Interval Estimation in Steady-State Simulations 436
  • 11.5.6 Confidence Intervals for Quantiles 440
  • 12 Comparison and Evaluation of Alternative System Designs 450
  • 12.1 Comparison of Two System Designs 451
  • 12.1.1 Independent Sampling with Equal Variances 454
  • 12.1.2 Independent Sampling with Unequal Variances 456
  • 12.1.3 Correlated Sampling, or Common Random Numbers 456
  • 12.1.4 Confidence Intervals with Specified Precision 466
  • 12.2 Comparison of Several System Designs 467
  • 12.2.1 Bonferroni Approach to Multiple Comparisons 468
  • 12.2.2 Bonferroni Approach to Selecting the Best 473
  • 12.3 Metamodeling 476
  • 12.3.1 Simple Linear Regression 477
  • 12.3.2 Testing for Significance of Regression 481
  • 12.3.3 Multiple Linear Regression 484
  • 12.3.4 Random-Number Assignment for Regression 484
  • 12.4 Optimization via Simulation 485
  • 12.4.1 What Does "Optimization via Simulation" Mean? 487
  • 12.4.2 Why Is Optimization via Simulation Difficult? 488
  • 12.4.3 Using Robust Heuristics 489
  • 12.4.4 An Illustration: Random Search 492
  • 13 Simulation of Manufacturing and Material Handling Systems 502
  • 13.1 Manufacturing and Material Handling Simulations 502
  • 13.1.1 Models of Manufacturing Systems 503
  • 13.1.2 Models of Material Handling 505
  • 13.1.3 Some Common Material Handling Equipment 506
  • 13.2 Goals and Performance Measures 507
  • 13.3 Issues in Manufacturing and Material Handling Simulations 508
  • 13.3.1 Modeling Downtimes and Failures 508
  • 13.3.2 Trace-Driven Models 513
  • 13.4 Case Studies of the Simulation of Manufacturing and Material Handling Systems 515
  • 14 Simulation of Computer Systems 528
  • 14.2 Simulation Tools 531
  • 14.2.1 Process Orientation 533
  • 14.2.2 Event Orientation 537
  • 14.3 Model Input 542
  • 14.3.1 Modulated Poisson Process 543
  • 14.3.2 Virtual Memory Referencing 547
  • 14.4 High-Level Computer-System Simulation 553
  • 14.5 CPU Simulation 557
  • 14.6 Memory Simulation 563
  • A.1 Random Digits 572
  • A.2 Random Normal Numbers 573
  • A.3 Cumulative Normal Distribution 574
  • A.4 Cumulative Poisson Distribution 576
  • A.5 Percentage Points of the Students t Distribution with v Degrees of Freedom 580
  • A.6 Percentage Points of the Chi-Square Distribution with v Degrees of Freedom 581
  • A.7 Percentage Points of the F Distribution with [alpha] = 0.05 582
  • A.8 Kolmogorov-Smirnov Critical Values 583
  • A.9 Maximum-Likelihood Estimates of the Gamma Distribution 584
  • A.10 Operating-Characteristic Curves for the Two-Sided t-Test for Different Values of Sample Size n 585
  • A.11 Operating-Characteristic Curves for the One-Sided t-Test for Different Values of Sample Size n 586.
Description
xiv, 594 p. : ill. ; 24 cm.
Notes
Includes bibliographical references and index.
Technical Details
  • Access in Virgo Classic

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    g| 1.1 t| When Simulation Is the Appropriate Tool g| 4 -- g| 1.2 t| When Simulation Is Not Appropriate g| 5 -- g| 1.3 t| Advantages and Disadvantages of Simulation g| 6 -- g| 1.4 t| Areas of Application g| 7 -- g| 1.5 t| Systems and System Environment g| 9 -- g| 1.6 t| Components of a System g| 10 -- g| 1.7 t| Discrete and Continuous Systems g| 12 -- g| 1.8 t| Model of a System g| 13 -- g| 1.9 t| Types of Models g| 13 -- g| 1.10 t| Discrete-Event System Simulation g| 14 -- g| 1.11 t| Steps in a Simulation Study g| 15 -- g| 2 t| Simulation Examples g| 23 -- g| 2.1 t| Simulation of Queueing Systems g| 24 -- g| 2.2 t| Simulation of Inventory Systems g| 41 -- g| 2.3 t| Other Examples of Simulation g| 47 -- g| 3.1 t| Concepts in Discrete-Event Simulation g| 64 -- g| 3.1.1 t| Event-Scheduling/Time-Advance Algorithm g| 67 -- g| 3.1.2 t| World Views g| 72 -- g| 3.1.3 t| Manual Simulation Using Event Scheduling g| 75 -- g| 3.2 t| List Processing g| 85 -- g| 3.2.1 t| Lists: Basic Properties and Operations g| 86 -- g| 3.2.2 t| Using Arrays for List Processing g| 87 -- g| 3.2.3 t| Using Dynamic Allocation and Linked Lists g| 90 -- g| 3.2.4 t| Advanced Techniques g| 92 -- g| 4 t| Simulation Software g| 95 -- g| 4.1 t| History of Simulation Software g| 96 -- g| 4.1.1 t| Period of Search (1955-60) g| 97 -- g| 4.1.2 t| Advent (1961-65) g| 97 -- g| 4.1.3 t| Formative Period (1966-70) g| 98 -- g| 4.1.4 t| Expansion Period (1971-78) g| 98 -- g| 4.1.5 t| Consolidation and Regeneration (1979-86) g| 99 -- g| 4.1.6 t| Present Period (1987-present) g| 99 -- g| 4.2 t| Selection of Simulation Software g| 100 -- g| 4.3 t| An Example Simulation g| 104 -- g| 4.4 t| Simulation in C++ g| 104 -- g| 4.5 t| Simulation in GPSS g| 114 -- g| 4.6 t| Simulation in CSIM g| 119 -- g| 4.7 t| Simulation Packages g| 123 -- g| 4.7.1 t| Arena g| 123 -- g| 4.7.2 t| AutoMod g| 124 -- g| 4.7.3 t| Deneb/QUEST g| 125 -- g| 4.7.4 t| Extend g| 126 -- g| 4.7.5 t| Micro Saint g| 127 -- g| 4.7.6 t| ProModel g| 127 -- g| 4.7.7 t| Taylor ED g| 128 -- g| 4.7.8 t| WITNESS g| 128 -- g| 4.8 t| Experimentation and Statistical Analysis Tools g| 129 -- g| 4.8.1 t| Common Features g| 129 -- g| 4.8.2 t| Analysis Tools g| 129 -- g| 4.9 t| Trends in Simulation Software g| 131 -- g| 4.9.1 t| High-Fidelity Simulation g| 131 -- g| 4.9.2 t| Data Exchange Standards g| 132 -- g| 4.9.3 t| Internet g| 132 -- g| 4.9.4 t| Old Paradigm versus New Paradigm g| 133 -- g| 4.9.5 t| Component Libraries g| 133 -- g| 4.9.6 t| Distributed Manufacturing Simulation/High Level Architecture g| 133 -- g| 4.9.7 t| Embedded Simulation g| 134 -- g| 4.9.8 t| Optimization g| 134 -- g| Part 2 t| Mathematical and Statistical Models -- g| 5 t| Statistical Models In Simulation g| 153 -- g| 5.2 t| Useful Statistical Models g| 160 -- g| 5.3 t| Discrete Distributions g| 165 -- g| 5.4 t| Continuous Distributions g| 170 -- g| 5.5 t| Poisson Process g| 190 -- g| 5.6 t| Empirical Distributions g| 193 -- g| 6 t| Queueing Models g| 204 -- g| 6.1 t| Characteristics of Queueing Systems g| 205 -- g| 6.1.1 t| Calling Population g| 206 -- g| 6.1.2 t| System Capacity g| 207 -- g| 6.1.3 t| Arrival Process g| 207 -- g| 6.1.4 t| Queue Behavior and Queue Discipline g| 209 -- g| 6.1.5 t| Service Times and the Service Mechanism g| 209 -- g| 6.2 t| Queueing Notation g| 211 -- g| 6.3 t| Long-Run Measures of Performance of Queueing Systems g| 212 -- g| 6.3.1 t| Time-Average Number in System L g| 213 -- g| 6.3.2 t| Average Time Spent in System per Customer, w g| 215 -- g| 6.3.3 t| Conservation Equation: L = [lambad]w g| 216 -- g| 6.3.4 t| Server Utilization g| 218 -- g| 6.3.5 t| Costs in Queueing Problems g| 223 -- g| 6.4 t| Steady-State Behavior of Infinite-Population Markovian Models g| 224 -- g| 6.4.1 t| Single-Server Queues with Poisson Arrivals and Unlimited Capacity: M/G/1 g| 225 -- g| 6.4.2 t| Multiserver Queue: M/M/c/[infinity]/[infinity] g| 231 -- g| 6.4.3 t| Multiserver Queues with Poisson Arrivals and Limited Capacity: M/M/c/N/[infinity] g| 237 -- g| 6.5 t| Steady-State Behavior of Finite-Population Models (M/M/c/K/K) g| 239 -- g| 6.6 t| Networks of Queues g| 243 -- g| Part 3 t| Random Numbers -- g| 7 t| Random-Number Generation g| 255 -- g| 7.1 t| Properties of Random Numbers g| 255 -- g| 7.2 t| Generation of Pseudo-Random Numbers g| 256 -- g| 7.3 t| Techniques for Generating Random Numbers g| 258 -- g| 7.3.1 t| Linear Congruential Method g| 258 -- g| 7.3.2 t| Combined Linear Congruential Generators g| 262 -- g| 7.4 t| Tests for Random Numbers g| 264 -- g| 7.4.1 t| Frequency Tests g| 266 -- g| 7.4.2 t| Runs Tests g| 270 -- g| 7.4.3 t| Tests for Autocorrelation g| 278 -- g| 7.4.4 t| Gap Test g| 281 -- g| 7.4.5 t| Poker Test g| 283 -- g| 8 t| Random-Variate Generation g| 289 -- g| 8.1 t| Inverse Transform Technique g| 290 -- g| 8.1.1 t| Exponential Distribution g| 290 -- g| 8.1.2 t| Uniform Distribution g| 294 -- g| 8.1.3 t| Weibull Distribution g| 294 -- g| 8.1.4 t| Triangular Distribution g| 295 -- g| 8.1.5 t| Empirical Continuous Distributions g| 296 -- g| 8.1.6 t| Continuous Distributions without a Closed-Form Inverse g| 300 -- g| 8.1.7 t| Discrete Distributions g| 301 -- g| 8.2 t| Direct Transformation for the Normal and Lognormal Distributions g| 307 -- g| 8.3 t| Convolution Method g| 309 -- g| 8.3.1 t| Erlang Distribution g| 309 -- g| 8.4 t| Acceptance-Rejection Technique g| 310 -- g| 8.4.1 t| Poisson Distribution g| 311 -- g| 8.4.2 t| Gamma Distribution g| 314 -- g| Part 4 t| Analysis of Simulation Data -- g| 9 t| Input Modeling g| 323 -- g| 9.1 t| Data Collection g| 324 -- g| 9.2 t| Identifying the Distribution with Data g| 327 -- g| 9.2.1 t| Histograms g| 327 -- g| 9.2.2 t| Selecting the Family of Distributions g| 331 -- g| 9.2.3 t| Quantile-Quantile Plots g| 333 -- g| 9.3 t| Parameter Estimation g| 336 -- g| 9.3.1 t| Preliminary Statistics: Sample Mean and Sample Variance g| 336 -- g| 9.3.2 t| Suggested Estimators g| 338 -- g| 9.4 t| Goodness-of-Fit Tests g| 343 -- g| 9.4.1 t| Chi-Square Test g| 343 -- g| 9.4.2 t| Chi-Square Test with Equal Probabilities g| 346 -- g| 9.4.3 t| Kolmogorov-Smirnov Goodness-of-Fit Test g| 348 -- g| 9.4.4 t| p-Values and "Best Fits" g| 350 -- g| 9.5 t| Selecting Input Models without Data g| 351 -- g| 9.6 t| Multivariate and Time-Series Input Models g| 353 -- g| 9.6.1 t| Covariance and Correlation g| 354 -- g| 9.6.2 t| Multivariate Input Models g| 354 -- g| 9.6.3 t| Time-Series Input Models g| 356 -- g| 10 t| Verification and Validation of Simulation Models g| 367 -- g| 10.1 t| Model Building, Verification, and Validation g| 368 -- g| 10.2 t| Verification of Simulation Models g| 369 -- g| 10.3 t| Calibration and Validation of Models g| 374 -- g| 10.3.1 t| Face Validity g| 376 -- g| 10.3.2 t| Validation of Model Assumptions g| 377 -- g| 10.3.3 t| Validating Input-Output Transformations g| 377 -- g| 10.3.4 t| Input-Output Validation: Using Historical Input Data g| 388 -- g| 10.3.5 t| Input-Ouput Validation: Using a Turing Test g| 392 -- g| 11 t| Output Analysis for a Single Model g| 398 -- g| 11.1 t| Types of Simulations with Respect to Output Analysis g| 399 -- g| 11.2 t| Stochastic Nature of Output Data g| 402 -- g| 11.3 t| Measures of Performance and Their Estimation g| 407 -- g| 11.3.1 t| Point Estimation g| 407 -- g| 11.3.2 t| Interval Estimation g| 409 -- g| 11.4 t| Output Analysis for Terminating Simulations g| 410 -- g| 11.4.1 t| Statistical Background g| 410 -- g| 11.4.2 t| Confidence-Interval Estimation for a Fixed Number of Replications g| 411 -- g| 11.4.3 t| Confidence Intervals with Specified Precision g| 414 -- g| 11.4.4 t| Confidence Intervals for Quantiles g| 416 -- g| 11.5 t| Output Analysis for Steady-State Simulations g| 418 -- g| 11.5.1 t| Initialization Bias in Steady-State Simulations g| 419 -- g| 11.5.2 t| Statistical Background g| 426 -- g| 11.5.3 t| Replication Method for Steady-State Simulations g| 430 -- g| 11.5.4 t| Sample Size in Steady-State Simulations g| 434 -- g| 11.5.5 t| Batch Means for Interval Estimation in Steady-State Simulations g| 436 -- g| 11.5.6 t| Confidence Intervals for Quantiles g| 440 -- g| 12 t| Comparison and Evaluation of Alternative System Designs g| 450 -- g| 12.1 t| Comparison of Two System Designs g| 451 -- g| 12.1.1 t| Independent Sampling with Equal Variances g| 454 -- g| 12.1.2 t| Independent Sampling with Unequal Variances g| 456 -- g| 12.1.3 t| Correlated Sampling, or Common Random Numbers g| 456 -- g| 12.1.4 t| Confidence Intervals with Specified Precision g| 466 -- g| 12.2 t| Comparison of Several System Designs g| 467 -- g| 12.2.1 t| Bonferroni Approach to Multiple Comparisons g| 468 -- g| 12.2.2 t| Bonferroni Approach to Selecting the Best g| 473 -- g| 12.3 t| Metamodeling g| 476 -- g| 12.3.1 t| Simple Linear Regression g| 477 -- g| 12.3.2 t| Testing for Significance of Regression g| 481 -- g| 12.3.3 t| Multiple Linear Regression g| 484 -- g| 12.3.4 t| Random-Number Assignment for Regression g| 484 -- g| 12.4 t| Optimization via Simulation g| 485 -- g| 12.4.1 t| What Does "Optimization via Simulation" Mean? g| 487 -- g| 12.4.2 t| Why Is Optimization via Simulation Difficult? g| 488 -- g| 12.4.3 t| Using Robust Heuristics g| 489 -- g| 12.4.4 t| An Illustration: Random Search g| 492 -- g| 13 t| Simulation of Manufacturing and Material Handling Systems g| 502 -- g| 13.1 t| Manufacturing and Material Handling Simulations g| 502 -- g| 13.1.1 t| Models of Manufacturing Systems g| 503 -- g| 13.1.2 t| Models of Material Handling g| 505 -- g| 13.1.3 t| Some Common Material Handling Equipment g| 506 -- g| 13.2 t| Goals and Performance Measures g| 507 -- g| 13.3 t| Issues in Manufacturing and Material Handling Simulations g| 508 -- g| 13.3.1 t| Modeling Downtimes and Failures g| 508 -- g| 13.3.2 t| Trace-Driven Models g| 513 -- g| 13.4 t| Case Studies of the Simulation of Manufacturing and Material Handling Systems g| 515 -- g| 14 t| Simulation of Computer Systems g| 528 -- g| 14.2 t| Simulation Tools g| 531 -- g| 14.2.1 t| Process Orientation g| 533 -- g| 14.2.2 t| Event Orientation g| 537 -- g| 14.3 t| Model Input g| 542 -- g| 14.3.1 t| Modulated Poisson Process g| 543 -- g| 14.3.2 t| Virtual Memory Referencing g| 547 -- g| 14.4 t| High-Level Computer-System Simulation g| 553 -- g| 14.5 t| CPU Simulation g| 557 -- g| 14.6 t| Memory Simulation g| 563 -- g| A.1 t| Random Digits g| 572 -- g| A.2 t| Random Normal Numbers g| 573 -- g| A.3 t| Cumulative Normal Distribution g| 574 -- g| A.4 t| Cumulative Poisson Distribution g| 576 -- g| A.5 t| Percentage Points of the Students t Distribution with v Degrees of Freedom g| 580 -- g| A.6 t| Percentage Points of the Chi-Square Distribution with v Degrees of Freedom g| 581 -- g| A.7 t| Percentage Points of the F Distribution with [alpha] = 0.05 g| 582 -- g| A.8 t| Kolmogorov-Smirnov Critical Values g| 583 -- g| A.9 t| Maximum-Likelihood Estimates of the Gamma Distribution g| 584 -- g| A.10 t| Operating-Characteristic Curves for the Two-Sided t-Test for Different Values of Sample Size n g| 585 -- g| A.11 t| Operating-Characteristic Curves for the One-Sided t-Test for Different Values of Sample Size n g| 586.
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    a| Banks, Jerry, d| 1939-
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    a| T57.62 .B35 2001 w| LC i| X004477111 l| STACKS m| SCI-ENG t| BOOK
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    a| T57.62 .B35 2001 w| LC i| X004683039 l| STACKS m| SCI-ENG t| BOOK

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