Discrete-Event System Simulation

Transcription

1 Discrete-Event System Simulation FOURTH EDITION Jerry Banks Independent Consultant John S. Carson II Brooks Automation Barry L. Nelson Northwestern University David M. Nicol University of Illinois, Urbana-Champaign PRENTICE-HALL INTERNATIONAL SERIES IN INDUSTRJAL AND SYSTEMS ENGINEERING W. J. Fabrycky and J. H. Mize, editors PEARSON Prentice Hall Pearson Education International

2 Contents Preface About the Authors VIII xv I Introduction to Discrete-Event System Simulation 1 Chapter 1 Introduction to Simulation When Simulation Is the Appropriate Tool When Simulation Is Not Appropriate Advantages and Disadvantages of Simulation Areas of Application Systems and System Environment Components of a System Discrete and Continuous Systems [[ 1.8 Model of a System Types of Models j Discrete-Event System Simulation ] Steps in a Simulation Study ]4 Chapter 2 Simulation Examples Simulation of Queueing Systems Simulation of Inventory Systems

3 VI Contents Other Examples of Simulation Summary Chapter General Principles Concepts in Discrete-Event Simulation The Event Scheduling/Time Advance Algorithm World Views Manual Simulation Using Event Scheduling List Processing Lists: Basic Properties and Operations Using Arrays for List Processing Using Dynamic Allocation and Linked Lists Advanced Techniques Summary Chapter Simulation Software History of Simulation Software 4.L1 The Period of Search ( ) The Advent ( ) The Formative Period ( ) The Expansion Period ( ) Consolidation and Regeneration ( ) Integrated Environments (1987-Present) Selection of Simulation Software An Example Simulation Simulation in Java Simulation in GPSS Simulation in SSF Simulation Software Arena AutoMod Extend Flexsim Micro Saint ProModel QUEST SIMUL WITNESS

4 CONTENTS vii 4.8 Experimentation and Statistical-Analysis Tools Common Features Products l 2 g 13j I j2 II Mathematical and Statistical Models 147 Chapter 5 Statistical Models in Simulation Review of Terminology and Concepts Useful Statistical Models Discrete Distributions Continuous Distributions igg 5.5 Poisson Process Properties of a Poisson Process Nonstationary Poisson Process Empirical Distributions Summary 193 Chapter 6 Queueing Models Characteristics of Queueing Systems The Calling Population System Capacity The Arrival Process Queue Behavior and Queue Discipline Service Times and the Service Mechanism Queueing Notation Long-Run Measures of Performance of Queueing Systems Time-Average Number in System L Average Time Spent in System Per Customer w The Conservation Equation: L = Xw Server Utilization Costs in Queueing Problems Steady-State Behavior of Tnfinite-Population Markovian Models Single-Server Queues with Poisson Arrivals and Unlimited Capacity: M/G/ Multiserver Queue: M/M/c/co/oo Multiserver Queues with Poisson Arrivals and Limited Capacity: M/M/c/N/co Steady-State ßehavior of Finite-Population Models (M/M/c/K/K)

5 VIII 6.6 Networks of Queues 6.7 Summary Contents III Random Numbers Chapter 7 Random-Number Generation 7.1 Properties of Random Numbers 7.2 Generation of Pseudo-Random Numbers 7.3 Techniques for Generating Random Numbers Linear Congruential Method Combined Linear Congruential Generators Random-Number Streams 7.4 Tests for Random Numbers Frequency Tests Tests for Autocorrelation 7.5 Summary Chapter 8 Random-Variate Generation Inverse-Transform Technique Exponential Distribution Uniform Distribution Weibull Distribution Triangulär Distribution Empirical Continuous Distributions Continuous Distributions without a Closed-Form Inverse Discrete Distributions Acceptance-Rejection Technique Poisson Distribution Nonstationary Poisson Process Gamma Distribution Special Properties -, Direct Transformation for the Normal and Lognormal Distributions Convolution Method More Special Properties 299 Summary

6 CONTENTS ix IV Analysis of Simulation Data 305 Chapter 9 Input Modeling Data Collection 3Q 9.2 Identifying the Distribution with Data Histograms Selecting the Family of Distributions Quantile-Quantüe Plots 31g 9.3 Parameter Estimation Preliminary Statistics: Sample Mean and Sample Variance Suggested Estimators Goodness-of-Fit Tests Chi-Square Test Chi-Square Test with Equal Probabilities Kolmogorov-Smirnov Goodness-of-Fit Test />-Values and "Best Fits" Fitting a Nonstationary Poisson Process Selecting Input Models without Data Multivariate and Time-Series Input Models Covariance and Correlation Multivariate Input Models 33g Time-Series Input Models The Normal-to-Anything Transformation Summary 344 Chapter 10 Verification and Validation of Simulation Models Model-Building, Verification, and Validation Verification of Simulation Models Calibration and Validation of Models Face Validity Validation of Model Assumptions Validating Input-Output Transformations Input-Output Validation: Using Historical Input Data Input-Output Validation: Using a Turing Test Summary 379 Chapter 11 Output Analysis for a Single Model Types of Simulation s with Respect to Output Analysis Stochastic Nature of Output Data

7 X Contents 11.3 Measures of Performance and Their Estiniation Point Esümation Confidence-Interval Estiniation Output Analysis for Terminating Simulations Statistical Background 394 1!.4.2 Confidence Intervals with Specified Precision Quantiles Estimating Probabilities and Quantiles from Summary Data 400 1!.5 Output Analysis for Steady-State Simulations Initialization Bias in Steady-State Simulations Error Esümation for Steady-State Simulation 409 1!.5.3 Replication Method for Steady-State Simulations Sample Size in Steady-State Simulations Batch Means for Interval Esümation in Steady-State Simulations Quantiles Summary 423 Chapter 12 Comparison and Evaluation of Alternative System Designs Comparison of Two System Designs Independent Sampting with Equal Variances Independent Sampimg with Unequal Variances Common Random Numbers (CRN) Confidence Intervals with Specified Precision Comparison of Several System Designs Bonferroni Approach to Multiple Comparisons Bonferroni Approach to Selecting the Best Bonferroni Approach to Screening Metamodeling Simple Linear Regression Testing for Significance of Regression Multiple Linear Regression Random-Number Assignment for Regression Optimization via Simulation What Does 'Optimization via Simulation' Mean? Why is Optimization via Simulation Difficult? Using Robust. Heuristics An Illustration: Random Search

8 CONTENTS xi 12.5 Summary g 477 V Applications 483 Chapter 13 Simulation of Manufacturing and Material-Handling Systems Manufacturing and Material-Handling Simulations Models of Manufacturing Systems Models of Material-Handling Some Common Material-Handling Equipment Goals and Performance Measures Issues in Manufacturing and Material-Handling Simulations Modeling Downtimes and Failures 49] Trace-Driven Models Case Studies of the Simulation of Manufacturing and Material-Handling Systems Manufacturing Example: A Job-Shop Simulation System Description and Model Assumptions Presimulation Analysis Simulation Mode! and Analysis of the Designed System Analysis of Station Utilization Analysis of Potential System Improvements Concluding Words Summary 50g 5Q5 507 Chapter 14 Simulation of Computer Systems Introduction 5 ] Simulation Tools Process Orientation Event Orientation Model Input Modulated Poisson Process Virtual-Memory Referencing High-Level Computer-System Simulation CPU Simulation 53g 14.6 Memory Simulation 543

9 XII Contents 14.7 Summary Chapter 15 Simulation of Computer Networks Introduction Traffic Modeling Mediti Access Control Token-Passing Protocols Ethernet Data Link Layer TCP Model Conslraction Construction Example Summary Appendix ^76 Index 591