仿真建模与分析 第3版 英文PDF|Epub|txt|kindle电子书版本网盘下载

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Chapter1 Basic Simulation Modeling1

1.1 The Nature of Simulation1

1.2 Systems,Models,and Simulation3

1.3 Discrete-Event Simulation6

1.3.1 Time-Advance Mechanisms7

1.3.2 Components and Organization of a Discrete-event Simulation Model9

1.4 Simulation of a Single-Server Queueing System12

1.4.1 Problem Statement12

List of Symbols17

1.4.2 Intuitive Explanation18

Preface19

1.4.3 Program Organization and Logic27

1.4.4 FORTRAN Program32

1.4.5 C Program41

1.4.6 Simulation Output and Discussion49

1.4.7 Alternative Stopping Rules51

1.4.8 Determining the Events and Variables57

1.5.1 Problem Statement60

1.5 Simulation of and Inventory System60

1.5.2 Program Organization and Logic62

1.5.3 FORTRAN Program66

1.5.4 C Program73

1.5.5 Simulation Output and Discussion78

1.6 Alternative Approaches to Modeling and Coding Simulations80

1.6.1 Parallel and Distributed Simulation80

1.6.2 Simulation across the Internet and Web-Based Simulation83

1.7 Steps in a Sound Simulation Study83

1.8 Other Types of Simulation87

1.8.1 Continuous Simulation87

1.8.2 Combined Discrete-Continuous Simulation89

1.8.3 monte Carlo Simulation90

1.9 Advantages,Disadvantages,and Pitfalls of Simulation91

Appendix 1A:Fixed-Increment Time Advance93

Appendix 1B:A Primer on Queueing Systems94

1B.2 Notation for Queueing Systems95

1B.1 Components of a Queueing System95

1B.3 Measures of Performance for Queueing Systems96

Problems99

Chapter2 Modeling Complex Systems106

2.1 Introduction106

2.2 List Processing in Simulation107

2.2.1 Approaches to Storing Lists in a Computer107

2.2.2 Linked Storage Allocation108

2.3 A Simple Simulation Language:simlib114

2.4.2 simlib Program123

2.4 Single-Server Queueing Simulation with simlib123

2.4.1 Problem Statement123

2.4.3 Simulation Output and Discussion128

2.5 Time-Shared Computer Model129

2.5.1 Problem Statement129

2.5.2 simlib Program130

2.5.3 Simulation Output and Discussion138

2.6.1 Problem Statement141

2.6 Multiteller Bank with Jockeying141

2.6.2 simlib Program142

2.6.3 Simulation Output and Discussion152

2.7 Job-Shop Model155

2.7.1 Problem Statement155

2.7.2 simlib Program157

2.7.3 Simulation Output and Discussion168

2.8 Efficient Event-List Manipulation170

Appendix2A: C Code for simlib171

Problems184

Chapter3 Simulation Software202

3.1 Introduction202

3.2 Comparison of Simulation Packages with Programming Languages203

3.3 Classification of Simulation Software204

3.3.1 General-Purpose Versus Application-oriented Simulation Packages204

3.3.2 Modeling Approaches205

3.3.3 Common Modeling Elements207

3.4.1 General Capabilities208

3.4 Desirable Software Features208

3.4.2 Hardware and Software Requirements210

3.4.3 Animation and Dynamic Graphics210

3.4.4 Statistical Capabilities212

3.4.5 Customer Support and Documentation213

3.4.6 Output Reports and Craphics214

3.5 General-Purpose Simulation Packages215

3.5.1 Arena215

3.5.2 Extend219

3.5.3 Other General-Purpose Simulation Packages225

3.6 Object-Oriented Simulation227

3.6.1 MODSIM III228

3.7 Examples of Application-Oriented Simulation Packages233

Chapter4 Review of Basic Probability and Statistics235

4.1 Introduction235

4.2 Random Variables and Their Properties235

4.3 Simulation Output Data and Stochastic Processes247

4.4 Estimation of Means,Variances,and Correlations249

4.5 Confidence Intervals and Hypothesis Tests for the Mean253

4.6 The Strong Law of Large Numbers259

4.7 The Danger of Replacing a Probability Distribution by its Mean260

Appendix4A:Comments on Covariance-Stationary Processes260

Problems261

Chapter5 Building Valid,Credible,and Appropriately Detailed Simulation Models264

5.1 Introduction and Definitions264

5.2 Guidelines for Determining the Level of Model Detail267

5.3 Verification of Simulation Computer Programs269

5.4 Techniques for Increasing Model Validity and Credibility273

5.4.1 Collect High-Quality Information and Data on the System274

5.4.2 Interact with the Manager on a Regular Basis275

5.4.3 Maintain and Assumptions Document and Perform a Structured Walk-Through276

5.4.4 Validate Components of the Model by Using Quantitative Techniques277

5.4.5 Validate the Output from the Overall Simulation Model279

5.4.6 Animation282

5.5 Management s Role in the Simulation Process282

5.6.1 Inspection Approach283

5.6 Statistical Procedures for Comparing Real-World Observations and Simulation Output Data283

5.6.2 Confidence-Interval Approach Based on Independent Data287

5.6.3 Time-Series Approaches289

Problems290

Chapter6 Selecting Input Probability Distributions292

6.1 Introduction292

6.2 Useful Probability Distributions298

6.2.1 Parameterization of Continuous Distributions298

6.2.2 Continuous Distributions299

6.2.3 Discrete Distributions318

6.2.4 Empirical Distributions318

6.3 Techniques for Assessing Sample Independence329

6.4 Activity Ⅰ:Hypothesizing Families of Distributions332

6.4.1 Summary Statistics333

6.4.2 Histograms335

6.4.3 Quantile Summaries and Box Plots337

6.5 ActivityⅡ：Estimation of Parameters343

6.6.1 Heuristic Procedures347

6.6 ActivityⅢ:Determining How Representative the Fitted Distributions Are347

6.6.2 Goodness-of-Fit Tests356

6.7 The ExpertFit Software and an Extended Example370

6.8 Shifted and Truncated Distributions376

6.9 Bezier Distributions378

6.10 Specifying Multivariate Distributions,Correlations,and Stochastic Processes378

6.10.1 Specifying Multivariate Distributions380

6.10.2 Specifying Arbitrary Marginal Distributions and Correlations383

6.10.3 Specifying Stochastic Processes384

6.11 Selecting a Distribution in the Absence of Data386

6.12 Models of Arrival Processes389

6.12.1 Poisson Processes389

6.12.2 Nonstationary Poisson Processes390

6.12.3 Batch Arrivals393

6.13 Assessing the Homogeneity of Different Data Sets394

Appendix 6A:Tables of MLEs for the Gamma and Beta Distributions395

Problems397

Chapter7 Random-Number Generators402

7.1 Introduction402

7.2 Linear Congruential Generators406

7.2.1 Mixed Generators409

7.2.2 Multiplicative Generators410

7.3 Other Kinds of Generators412

7.3.1 More General Congruences413

7.3.2 Composite Generators414

7.3.3 Tausworthe and Related Generators416

7.4 Testing Random-Number Generators417

7.4.1 Empirical Tests418

7.4.2 Theoretical Tests423

7.4.3 Some General Observations on Testing426

Appendix7A:Portable Computer Codes for a PMMLCG427

7A.1 FORTRAN428

7A.2 C430

7A.3 Obtaining Initial Seeds for the Streams431

Appendix 7B:Portable C Code for a Combined MRG432

Problems435

8.1 Introduction437

Chapter8 Generating Random Variates437

8.2 General Approaches to Generating Random Variates439

8.2.1 Inverse Transform440

8.2.2 Composition448

8.2.3 Convolution451

8.2.4 Acceptance-Rejection452

8.2.5 Special Properties459

8.3 Generating Continuous Random Variates459

8.3.1 Uniform460

8.3.2 Exponential460

8.3.3 m-Erlang461

8.3.4 Gamma461

8.3.5 Weibull464

8.3.6 Normal465

8.3.7 Lognormal466

8.3.8 Beta467

8.3.12 Johnson Bounded468

8.3.11 Log-Logistic468

8.3.9 Pearson Type V468

8.3.10 Pearson Type VI468

8.3.13 Johnson Unbounded469

8.3.14 Bezier469

8.3.15 Triangular469

8.3.16 Empirical Distributions470

8.4 Generating Discrete Random Variates471

8.4.2 Discrete Uniform472

8.4.3 Arbitrary Discrete Distribution472

8.4.1 Bernoulli472

8.4.4 Binonial477

8.4.5 Geometric477

8.4.6 Negative Binomial477

8.4.7 Poisson478

8.5 Generating Random Vectors,Correlated Random Variates,and Stochastic Processes478

8.5.1 Using Conditional Distributions479

8.5.2 Multivariate Normal and Multivariate Lognormal480

8.5.3 Correlated Gamma Random Variates481

8.5.5 Generating Random Vectors with Arbitrarily Specified Marginal Distributions and Correlations482

8.5.4 Generating from Multivariate Families482

8.5.6 Generating Stochastic Processes483

8.6 Generating Arrival Processes484

8.6.1 Poisson Processes485

8.6.2 Nonstationary Poisson Processes485

8.6.3 Batch Arrivals489

Appendix8A:Validity of the Acceptance-Rejection Method489

Appendix8B:Setup for the Alias Method490

Problems491

9.1 Introduction496

Chapter9 Output Data Analysis for a Single System496

9.2 Transient and Steady-State Behavior of a Stochastic Process499

9.3 Types of Simulations with Regard to Output Analysis502

9.4 Statistical Analysis for Terminating Simulations505

9.4.1 Estimating Means506

9.4.2 Estimating Other Measures of Performance515

9.4.3 Choosing Initial Conditions518

9.5 Statistical Analysis for Steady-State Parameters518

9.5.1 The Problem of the Initial Transient519

9.5.2 Replicfation/Daletion Approaches for Means525

9.5.3 Other Approaches for Means527

9.5.4 Estimating Other Measures of Performance537

9.6 Statistical Analysis for Steady-State Cycle Parameters539

9.7 Multiple Measures of Performance542

9.8 Time Plots of Important Variables545

Appendix9A:Ratios of Expectations and Jackknife Estimators545

Problems547

10.1 Introduction553

Chapter10 Comparing Alternative System Configurations553

10.2 Confidence Intervals for the Difference Between the Expected Responses of Two Systems557

10.2.1 A Paired-t Confidence Interval557

10.2.2 A Modified Two-Sample-t Confidence Interval559

10.2.3 Contrasting the Two Methods560

10.2.4 Comparisons Based on Steady-State Measures of Performance560

10.3 Confidence Intervals for Comparing More than Two Systems562

10.3.1 Comparisons with a Standard563

10.3.2 All Pairwise Comparisons564

10.4 Ranking and Selection566

10.3.3 Multiple Comparisons with the Best566

10.4.2 Selecting a Subset of Size m Containing the Best of k Systems569

10.4.3 Selecting the m Best of k Systems570

10.4.4 Additional Problems and Methods572

Appendix 10A:Validity of the Selection Procedures575

Appendix 10B:Constants for the Selection Procedures576

Problems579

Chapter11 Variance-Reduction Techniques581

11.1 Introduction581

11.2 Common Random Numbers582

11.2.1 Rationale583

11.2.2 Applicability584

11.2.3 Synchronization586

11.2.4 Some Examples590

10.4.1 Selecting the Best of k Systems597

11.3 Antithetic Variates598

11.4 Control Variates604

11.5 Indirect Estimation611

11.6 Conditioning613

Problems617

Chapter12 Experimental Design,Sensitivity Analysis,and Optimization622

12.1 Introduction622

12.2 2k Factorial Designs625

12.3 Coping with Many Factors637

12.3.1 2k-p Fractional Factorial Designs638

12.3.2 Factor-Screening Strategies644

12.4 Response Surfaces and Metamodels646

12.5 Sensitivity and Gradient Estimation655

12.6 Optimum Seeking657

12.6.1 Optimum-Seeking Methods659

12.6.2 Optimum-Seeking Packages Interfaced with Simulation Software662

Problems666

Chapter13 Simulation of Manufacturing Systems669

13.1 Introduction669

13.2 Objectives of Simulation in Manufacturing670

13.3 Simulation Software for Manufacturing Applications672

13.4 Modeling System Randomness675

13.4.1 Sources of Randomness675

13.4.2 Machine Downtimes678

13.5 An Extended Example684

13.5.1 Problem Description and Simulation Results684

13.5.2 Statistical Calculations693

13.6.1 Description of the System695

13.6 A Simulation Case Study of a Metal-Parts Manufacturing Facility695

13.6.2 Overall Objectives and Issues to Be Investigated696

13.6.3 Development of the Model696

13.6.4 Model Verification and Validation697

13.6.5 Results of the Simulation Experiments699

13.6.6 Conclusions and Benefits701

Problems702

Appendix707

References711

Subject Index745