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Discrete-Event System Simulation

ISBN: 9780131446793 | 0131446797
Edition: 5th
Format: Paperback
Publisher: Prentice Hall
Pub. Date: 1/1/2010

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SummaryTable of Contents
David M. Nicol is professor of electrical and computer engineering at the University of Illinois at Urbana-Champaign.
This book provides a basic treatment of discrete-event simulation, including the proper collection and analysis of data, the use of analytic techniques, verification and validation of models, and designing simulation experiments.Contains up-to-date treatment of simulation of manufacturing and material handling systems. Includes numerous solved examples. Offers an integrated website. Explains how to ... MORE
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Prefacexiii
About the Authorsxv
I Introduction to Discrete-Event System Simulation1(146)
Chapter 1 Introduction to Simulation
3(18)
1.1 When Simulation Is the Appropriate Tool
4(1)
1.2 When Simulation Is Not Appropriate
4(1)
1.3 Advantages and Disadvantages of Simulation
5(2)
1.4 Areas of Application
7(2)
1.5 Systems and System Environment
9(1)
1.6 Components of a System
9(2)
1.7 Discrete and Continuous Systems
11(1)
1.8 Model of a System
12(1)
1.9 Types of Models
13(1)
1.10 Discrete-Event System Simulation
13(1)
1.11 Steps in a Simulation Study
14(4)
References
18(1)
Exercises
19(2)
Chapter 2 Simulation Examples
21(46)
2.1 Simulation of Queueing Systems
22(17)
2.2 Simulation of Inventory Systems
39(7)
2.3 Other Examples of Simulation
46(11)
2.4 Summary
57(1)
References
57(1)
Exercises
57(10)
Chapter 3 General Principles
67(28)
3.1 Concepts in Discrete-Event Simulation
68(18)
3.1.1 The Event Scheduling/Time Advance Algorithm
71(3)
3.1.2 World Views
74(3)
3.1.3 Manual Simulation Using Event Scheduling
77(9)
3.2 List Processing
86(6)
3.2.1 Lists: Basic Properties and Operations
87(1)
3.2.2 Using Arrays for List Processing
88(2)
3.2.3 Using Dynamic Allocation and Linked Lists
90(2)
3.2.4 Advanced Techniques
92(1)
3.3 Summary
92(1)
References
92(1)
Exercises
93(2)
Chapter 4 Simulation Software
95(52)
4.1 History of Simulation Software
96(3)
4.1.1 The Period of Search (1955-60)
97(1)
4.1.2 The Advent (1961-65)
97(1)
4.1.3 The Formative Period (1966-70)
97(1)
4.1.4 The Expansion Period (1971-78)
98(1)
4.1.5 Consolidation and Regeneration (1979-86)
98(1)
4.1.6 Integrated Environments (1987-Present)
99(1)
4.2 Selection of Simulation Software
99(3)
4.3 An Example Simulation
102(2)
4.4 Simulation in Java
104(8)
4.5 Simulation in GPSS
112(5)
4.6 Simulation in SSF
117(3)
4.7 Simulation Software
120(8)
4.7.1 Arena
122(1)
4.7.2 AutoMod
123(1)
4.7.3 Extend
124(1)
4.7.4 Flexsim
124(1)
4.7.5 Micro Saint
125(1)
4.7.6 ProModel
125(1)
4.7.7 QUEST
126(1)
4.7.8 SIMUL8
127(1)
4.7.9 WITNESS
128(1)
4.8 Experimentation and Statistical-Analysis Tools
128(3)
4.8.1 Common Features
128(1)
4.8.2 Products
129(2)
References
131(1)
Exercises
132(15)
II Mathematical and Statistical Models147(102)
Chapter 5 Statistical Models in Simulation
149(52)
5.1 Review of Terminology and Concepts
150(6)
5.2 Useful Statistical Models
156(4)
5.3 Discrete Distributions
160(6)
5.4 Continuous Distributions
166(20)
5.5 Poisson Process
186(4)
5.5.1 Properties of a Poisson Process
188(1)
5.5.2 Nonstationary Poisson Process
189(1)
5.6 Empirical Distributions
190(3)
5.7 Summary
193(1)
References
193(1)
Exercises
193(8)
Chapter 6 Queueing Models
201(48)
6.1 Characteristics of Queueing Systems
202(6)
6.1.1 The Calling Population
202(2)
6.1.2 System Capacity
204(1)
6.1.3 The Arrival Process
204(1)
6.1.4 Queue Behavior and Queue Discipline
205(1)
6.1.5 Service Times and the Service Mechanism
206(2)
6.2 Queueing Notation
208(1)
6.3 Long-Run Measures of Performance of Queueing Systems
208(12)
6.3.1 Time-Average Number in System L
209(2)
6.3.2 Average Time Spent in System Per Customer w
211(1)
6.3.3 The Conservation Equation: L = λw
212(1)
6.3.4 Server Utilization
213(5)
6.3.5 Costs in Queueing Problems
218(2)
6.4 Steady-State Behavior of Infinite-Population Markovian Models
220(15)
6.4.1 Single-Server Queues with Poisson Arrivals and Unlimited Capacity: M/G/1
221(6)
6.4.2 Multiserver Queue: M/M/c/infinity/infinity
227(6)
6.4.3 Multiserver Queues with Poisson Arrivals and Limited Capacity: M/M/c/N/infinity
233(2)
6.5 Steady-State Behavior of Finite-Population Models (M/M/c/K/K)
235(4)
6.6 Networks of Queues
239(2)
6.7 Summary
241(1)
References
242(1)
Exercises
243(6)
III Random Numbers249(56)
Chapter 7 Random-Number Generation
251(21)
7.1 Properties of Random Numbers
251(1)
7.2 Generation of Pseudo-Random Numbers
252(1)
7.3 Techniques for Generating Random Numbers
253(7)
7.3.1 Linear Congruential Method
254(3)
7.3.2 Combined Linear Congruential Generators
257(2)
7.3.3 Random-Number Streams
259(1)
7.4 Tests for Random Numbers
260(7)
7.4.1 Frequency Tests
261(4)
7.4.2 Tests for Autocorrelation
265(2)
7.5 Summary
267(1)
References
268(1)
Exercises
269(3)
Chapter 8 Random-Variate Generation
272(33)
8.1 Inverse-Transform Technique
273(16)
8.1.1 Exponential Distribution
273(3)
8.1.2 Uniform Distribution
276(1)
8.1.3 Weibull Distribution
277(1)
8.1.4 Triangular Distribution
278(1)
8.1.5 Empirical Continuous Distributions
279(4)
8.1.6 Continuous Distributions without a Closed-Form Inverse
283(1)
8.1.7 Discrete Distributions
284(5)
8.2 Acceptance-Rejection Technique
289(7)
8.2.1 Poisson Distribution
290(3)
8.2.2 Nonstationary Poisson Process
293(1)
8.2.3 Gamma Distribution
294(2)
8.3 Special Properties
296(3)
8.3.1 Direct Transformation for the Normal and Lognormal Distributions
296(2)
8.3.2 Convolution Method
298(1)
8.3.3 More Special Properties
299(1)
8.4 Summary
299(1)
References
299(1)
Exercises
300(5)
IV Analysis of Simulation Data305(178)
Chapter 9 Input Modeling
307(47)
9.1 Data Collection
308(2)
9.2 Identifying the Distribution with Data
310(9)
9.2.1 Histograms
310(3)
9.2.2 Selecting the Family of Distributions
313(3)
9.2.3 Quantile-Quantile Plots
316(3)
9.3 Parameter Estimation
319(7)
9.3.1 Preliminary Statistics: Sample Mean and Sample Variance
319(2)
9.3.2 Suggested Estimators
321(5)
9.4 Goodness-of-Fit Tests
326(8)
9.4.1 Chi-Square Test
327(2)
9.4.2 Chi-Square Test with Equal Probabilities
329(2)
9.4.3 Kolmogorov-Smirnov Goodness-of-Fit Test
331(2)
9.4.4 ρ-Values and "Best Fits"
333(1)
9.5 Fitting a Nonstationary Poisson Process
334(1)
9.6 Selecting Input Models without Data
335(2)
9.7 Multivariate and Time-Series Input Models
337(7)
9.7.1 Covariance and Correlation
337(1)
9.7.2 Multivariate Input Models
338(2)
9.7.3 Time-Series Input Models
340(2)
9.7.4 The Normal-to-Anything Transformation
342(2)
9.8 Summary
344(1)
References
345(1)
Exercises
346(8)
Chapter 10 Verification and Validation of Simulation Models
354(29)
10.1 Model-Building, Verification, and Validation
355(1)
10.2 Verification of Simulation Models
356(5)
10.3 Calibration and Validation of Models
361(18)
10.3.1 Face Validity
362(1)
10.3.2 Validation of Model Assumptions
362(1)
10.3.3 Validating Input-Output Transformations
363(11)
10.3.4 Input-Output Validation: Using Historical Input Data
374(4)
10.3.5 Input-Output Validation: Using a Turing Test
378(1)
10.4 Summary
379(1)
References
379(2)
Exercises
381(2)
Chapter 11 Output Analysis for a Single Model
383(49)
11.1 Types of Simulations with Respect to Output Analysis
384(3)
11.2 Stochastic Nature of Output Data
387(3)
11.3 Measures of Performance and Their Estimation
390(3)
11.3.1 Point Estimation
390(2)
11.3.2 Confidence-Interval Estimation
392(1)
11.4 Output Analysis for Terminating Simulations
393(9)
11.4.1 Statistical Background
394(3)
11.4.2 Confidence Intervals with Specified Precision
397(2)
11.4.3 Quantiles
399(1)
11.4.4 Estimating Probabilities and Quantiles from Summary Data
400(2)
11.5 Output Analysis for Steady-State Simulations
402(21)
11.5.1 Initialization Bias in Steady-State Simulations
403(6)
11.5.2 Error Estimation for Steady-State Simulation
409(4)
11.5.3 Replication Method for Steady-State Simulations
413(4)
11.5.4 Sample Size in Steady-State Simulations
417(1)
11.5.5 Batch Means for Interval Estimation in Steady-State Simulations
418(4)
11.5.6 Quantiles
422(1)
11.6 Summary
423(1)
References
423(1)
Exercises
424(8)
Chapter 12 Comparison and Evaluation of Alternative System Designs
432(51)
12.1 Comparison of Two System Designs
433(15)
12.1.1 Independent Sampling with Equal Variances
436(2)
12.1.2 Independent Sampling with Unequal Variances
438(1)
12.1.3 Common Random Numbers (CRN)
438(8)
12.1.4 Confidence Intervals with Specified Precision
446(2)
12.2 Comparison of Several System Designs
448(10)
12.2.1 Bonferroni Approach to Multiple Comparisons
449(5)
12.2.2 Bonferroni Approach to Selecting the Best
454(3)
12.2.3 Bonferroni Approach to Screening
457(1)
12.3 Metamodeling
458(9)
12.3.1 Simple Linear Regression
459(4)
12.3.2 Testing for Significance of Regression
463(3)
12.3.3 Multiple Linear Regression
466(1)
12.3.4 Random-Number Assignment for Regression
466(1)
12.4 Optimization via Simulation
467(9)
12.4.1 What Does `Optimization via Simulation' Mean?
468(1)
12.4.2 Why is Optimization via Simulation Difficult?
469(1)
12.4.3 Using Robust Heuristics
470(3)
12.4.4 An Illustration: Random Search
473(3)
12.5 Summary
476(1)
References
476(1)
Exercises
477(6)
V Applications483(93)
Chapter 13 Simulation of Manufacturing and Material-Handling Systems
485(32)
13.1 Manufacturing and Material-Handling Simulations
486(3)
13.1.1 Models of Manufacturing Systems
486(1)
13.1.2 Models of Material-Handling
487(1)
13.1.3 Some Common Material-Handling Equipment
488(1)
13.2 Goals and Performance Measures
489(1)
13.3 Issues in Manufacturing and Material-Handling Simulations
490(6)
13.3.1 Modeling Downtimes and Failures
491(4)
13.3.2 Trace-Driven Models
495(1)
13.4 Case Studies of the Simulation of Manufacturing and Material-Handling Systems
496(3)
13.5 Manufacturing Example: A Job-Shop Simulation
499(7)
13.5.1 System Description and Model Assumptions
499(3)
13.5.2 Presimulation Analysis
502(1)
13.5.3 Simulation Model and Analysis of the Designed System
503(1)
13.5.4 Analysis of Station Utilization
503(1)
13.5.5 Analysis of Potential System Improvements
504(2)
13.5.6 Concluding Words
506(1)
13.6 Summary
506(1)
References
506(1)
Exercises
507(10)
Chapter 14 Simulation of Computer Systems
517(33)
14.1 Introduction
517(3)
14.2 Simulation Tools
520(5)
14.2.1 Process Orientation
522(2)
14.2.2 Event Orientation
524(1)
14.3 Model Input
525(13)
14.3.1 Modulated Poisson Process
526(2)
14.3.2 Virtual-Memory Referencing
528(6)
14.4 High-Level Computer-System Simulation
534(4)
14.5 CPU Simulation
538(5)
14.6 Memory Simulation
543(3)
14.6 Summary
546(1)
References
546(1)
Exercises
547(3)
Chapter 15 Simulation of Computer Networks
550(26)
15.1 Introduction
550(2)
15.2 Traffic Modeling
552(3)
15.3 Media Access Control
555(6)
15.3.1 Token-Passing Protocols
556(3)
15.3.2 Ethernet
559(2)
15.4 Data Link Layer
561(1)
15.5 TCP
562(7)
15.6 Model Construction
569(4)
15.6.1 Construction
569(2)
15.6.2 Example
571(2)
15.7 Summary
573(1)
References
574(1)
Exercises
574(2)
Appendix576(15)
Index591

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