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Author:   Jerry Banks ,  John Carson ,  Barry Nelson ,  David Nicol
Publisher:   Pearson Education Limited
Imprint:   Pearson Education Limited
Edition:   5th edition
Dimensions:   Width: 21.60cm , Height: 2.50cm , Length: 27.90cm
Weight:   1.225kg
ISBN:  

9781292024370

ISBN 10:   1292024372
Pages:   568
Publication Date:   17 July 2013
Audience:   College/higher education ,  Tertiary & Higher Education
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

I Introduction to Discrete-Event System Simulation 1Chapter 1 Introduction to Simulation 31.1 When Simulation Is the Appropriate Tool 41.2 When Simulation Is Not Appropriate 41.3 Advantages and Disadvantages of Simulation 51.4 Areas of Application 71.5 Systems and System Environment 91.6 Components of a System 91.7 Discrete and Continuous Systems 111.8 Model of a System 121.9 Types of Models 131.10 Discrete-Event System Simulation 131.11 Steps in a Simulation Study 14References 18Exercises 19 Chapter 2 Simulation Examples 212.1 Simulation of Queueing Systems 222.2 Simulation of Inventory Systems 392.3 Other Examples of Simulation 462.4 Summary 57References 57Exercises 57 Chapter 3 General Principles 673.1 Concepts in Discrete-Event Simulation 683.1.1 The Event Scheduling/Time Advance Algorithm 713.1.2 World Views 743.1.3 Manual Simulation Using Event Scheduling 773.2 List Processing 863.2.1 Lists: Basic Properties and Operations 873.2.2 Using Arrays for List Processing 883.2.3 Using Dynamic Allocation and Linked Lists 903.2.4 Advanced Techniques 923.3 Summary 92References 92Exercises 93 Chapter 4 Simulation Software 954.1 History of Simulation Software 964.1.1 The Period of Search (1955-60) 974.1.2 The Advent (1961-65) 974.1.3 The Formative Period (1966-70) 974.1.4 The Expansion Period (1971-78) 984.1.5 Consolidation and Regeneration (1979-86) 984.1.6 Integrated Environments (1987-Present) 994.2 Selection of Simulation Software 994.3 An Example Simulation 1024.4 Simulation in Java 1044.5 Simulation in GPSS 1124.6 Simulation in SSF 1174.7 Simulation Software 1204.7.1 Arena 1224.7.2 AutoMod 1234.7.3 Extend 1244.7.4 Flexsim 1244.7.5 Micro Saint 1254.7.6 ProModel 1254.7.7 QUEST 1264.7.8 SIMUL8 1274.7.9 WITNESS 1284.8 Experimentation and Statistical-Analysis Tools 1284.8.1 Common Features 1284.8.2 Products 129References 131Exercises 132 II Mathematical and Statistical Models 147Chapter 5 Statistical Models in Simulation 1495.1 Review of Terminology and Concepts 1505.2 Useful Statistical Models 1565.3 Discrete Distributions 1605.4 Continuous Distributions 1665.5 Poisson Process 1865.5.1 Properties of a Poisson Process 1885.5.2 Nonstationary Poisson Process 1895.6 Empirical Distributions 1905.7 Summary 193References 193Exercises 193 Chapter 6 Queueing Models 2016.1 Characteristics of Queueing Systems 2026.1.1 The Calling Population 2026.1.2 System Capacity 2046.1.3 The Arrival Process 2046.1.4 Queue Behavior and Queue Discipline 2056.1.5 Service Times and the Service Mechanism 2066.2 Queueing Notation 2086.3 Long-Run Measures of Performance of Queueing Systems 2086.3.1 Time-Average Number in System L 2096.3.2 Average Time Spent in System Per Customer w 2116.3.3 The Conservation Equation: L = λw 2126.3.4 Server Utilization 2136.3.5 Costs in Queueing Problems 2186.4 Steady-State Behavior of Infinite-Population Markovian Models 2206.4.1 Single-Server Queues with Poisson Arrivals and Unlimited Capacity: M/G/1 2216.4.2 Multiserver Queue: M/M/c/â /â 2276.4.3 Multiserver Queues with Poisson Arrivals and Limited Capacity: M/M/c/N/â 2336.5 Steady-State Behavior of Finite-Population Models (M/M/c/K/K) 2356.6 Networks of Queues 2396.7 Summary 241References 242Exercises 243 III Random Numbers 249Chapter 7 Random-Number Generation 2517.1 Properties of Random Numbers 2517.2 Generation of Pseudo-Random Numbers 2527.3 Techniques for Generating Random Numbers 2537.3.1 Linear Congruential Method 2547.3.2 Combined Linear Congruential Generators 2577.3.3 Random-Number Streams 2597.4 Tests for Random Numbers 2607.4.1 Frequency Tests 2617.4.2 Tests for Autocorrelation 2657.5 Summary 267References 268Exercises 269 Chapter 8 Random-Variate Generation 2728.1 Inverse-Transform Technique 2738.1.1 Exponential Distribution 2738.1.2 Uniform Distribution 2768.1.3 Weibull Distribution 2778.1.4 Triangular Distribution 2788.1.5 Empirical Continuous Distributions 2798.1.6 Continuous Distributions without a Closed-Form Inverse 2838.1.7 Discrete Distributions 2848.2 Acceptance-Rejection Technique 2898.2.1 Poisson Distribution 2908.2.2 Nonstationary Poisson Process 2938.2.3 Gamma Distribution 2948.3 Special Properties 2968.3.1 Direct Transformation for the Normal and Lognormal Distributions 2968.3.2 Convolution Method 2988.3.3 More Special Properties 2998.4 Summary 299References 299Exercises 300 IV Analysis of Simulation Data 305Chapter 9 Input Modeling 3079.1 Data Collection 3089.2 Identifying the Distribution with Data 3109.2.1 Histograms 3109.2.2 Selecting the Family of Distributions 3139.2.3 Quantile-Quantile Plots 3169.3 Parameter Estimation 3199.3.1 Preliminary Statistics: Sample Mean and Sample Variance 3199.3.2 Suggested Estimators 3219.4 Goodness-of-Fit Tests 3269.4.1 Chi-Square Test 3279.4.2 Chi-Square Test with Equal Probabilities 3299.4.3 Kolmogorov-Smirnov Goodness-of-Fit Test 3319.4.4 p-Values and Best Fits 3339.5 Fitting a Nonstationary Poisson Process 3349.6 Selecting Input Models without Data 3359.7 Multivariate and Time-Series Input Models 3379.7.1 Covariance and Correlation 3379.7.2 Multivariate Input Models 3389.7.3 Time-Series Input Models 3409.7.4 The Normal-to-Anything Transformation 3429.8 Summary 344References 345Exercises 346 Chapter 10 Verification and Validation of Simulation Models 35410.1 Model-Building, Verification, and Validation 35510.2 Verification of Simulation Models 35610.3 Calibration and Validation of Models 36110.3.1 Face Validity 36210.3.2 Validation of Model Assumptions 36210.3.3 Validating Input-Output Transformations 36310.3.4 Input-Output Validation: Using Historical Input Data 37410.3.5 Input-Output Validation: Using a Turing Test 37810.4 Summary 379References 379Exercises 381 Chapter 11 Output Analysis for a Single Model 38311.1 Types of Simulations with Respect to Output Analysis 38411.2 Stochastic Nature of Output Data 38711.3 Measures of Performance and Their Estimation 39011.3.1 Point Estimation 39011.3.2 Confidence-Interval Estimation 39211.4 Output Analysis for Terminating Simulations 39311.4.1 Statistical Background 39411.4.2 Confidence Intervals with Specified Precision 39711.4.3 Quantiles 39911.4.4 Estimating Probabilities and Quantiles from Summary Data 40011.5 Output Analysis for Steady-State Simulations 40211.5.1 Initialization Bias in Steady-State Simulations 40311.5.2 Error Estimation for Steady-State Simulation 40911.5.3 Replication Method for Steady-State Simulations 41311.5.4 Sample Size in Steady-State Simulations 41711.5.5 Batch Means for Interval Estimation in Steady-State Simulations 41811.5.6 Quantiles 42211.6 Summary 423References 423Exercises 424 Chapter 12 Comparison and Evaluation of Alternative System Designs 43212.1 Comparison of Two System Designs 43312.1.1 Independent Sampling with Equal Variances 43612.1.2 Independent Sampling with Unequal Variances 43812.1.3 Common Random Numbers (CRN) 43812.1.4 Confidence Intervals with Specified Precision 44612.2 Comparison of Several System Designs 44812.2.1 Bonferroni Approach to Multiple Comparisons 44912.2.2 Bonferroni Approach to Selecting the Best 45412.2.3 Bonferroni Approach to Screening 45712.3 Metamodeling 45812.3.1 Simple Linear Regression 45912.3.2 Testing for Significance of Regression 46312.3.3 Multiple Linear Regression 46612.3.4 Random-Number Assignment for Regression 46612.4 Optimization via Simulation 46712.4.1 What Does 'Optimization via Simulation' Mean? 46812.4.2 Why is Optimization via Simulation Difficult? 46912.4.3 Using Robust Heuristics 47012.4.4 An Illustration: Random Search 47312.5 Summary 476References 476Exercises 477 V Applications 483Chapter 13 Simulation of Manufacturing and Material-Handling Systems 48513.1 Manufacturing and Material-Handling Simulations 48613.1.1 Models of Manufacturing Systems 48613.1.2 Models of Material-Handling 48713.1.3 Some Common Material-Handling Equipment 48813.2 Goals and Performance Measures 48913.3 Issues in Manufacturing and Material-Handling Simulations 49013.3.1 Modeling Downtimes and Failures 49113.3.2 Trace-Driven Models 49513.4 Case Studies of the Simulation of Manufacturing and Material-Handling Systems 49613.5 Manufacturing Example: A Job-Shop Simulation 49913.5.1 System Description and Model Assumptions 49913.5.2 Presimulation Analysis 50213.5.3 Simulation Model and Analysis of the Designed System 50313.5.4 Analysis of Station Utilization 50313.5.5 Analysis of Potential System Improvements 50413.5.6 Concluding Words 50613.6 Summary 506References 506Exercises 507 Chapter 14 Simulation of Computer Networks 55015.1 Introduction 55015.2 Traffic Modeling 55215.3 Media Access Control 55515.3.1 Token-Passing Protocols 55615.3.2 Ethernet 55915.4 Data Link Layer 56115.5 TCP 56215.6 Model Construction 56915.6.1 Construction 56915.6.2 Example 57115.7 Summary 573References 574Exercises 574Appendix 576Index 591

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