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Discrete-Event System Simulation

ISBN: 9780130887023 | 0130887021
Edition: 3rd
Format: Hardcover
Publisher: Prentice Hall
Pub. Date: 1/1/2001

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SummaryTable of Contents
Appropriate for junior- senior-level Simulation courses in departments of Engineering, Management and Computer Science; or a second course in Simulation. This text provides a basic treatment of discrete-event simulation, one of the most widely used operations research and management science tools for dealing with system design in the presence of uncertainty. Proper collection and analysis of data, use of analytic techniques, verification and validation of models and the appropriate design of simulation experiments are treated extensively. Readi... MORE
Prefacexi
About the Authorsxiii
PART ONE: Introduction to Discrete-Event System Simulation
Introduction to Simulation
3(20)
... MOREWhen Simulation Is the Appropriate Tool
4(1)
When Simulation Is Not Appropriate
5(1)
Advantages and Disadvantages of Simulation
6(1)
Areas of Application
7(2)
Systems and System Environment
9(1)
Components of a System
10(2)
Discrete and Continuous Systems
12(1)
Model of a System
13(1)
Types of Models
13(1)
Discrete-Event System Simulation
14(1)
Steps in a Simulation Study
15(8)
References
20(1)
Exercises
21(2)
Simulation Examples
23(40)
Simulation of Queueing Systems
24(17)
Simulation of Inventory Systems
41(6)
Other Examples of Simulation
47(8)
Summary
55(8)
References
55(1)
Exercises
56(7)
General Principles
63(32)
Concepts in Discrete-Event Simulation
64(21)
The Event-Scheduling/Time-Advance Algorithm
67(5)
World Views
72(3)
Manual Simulation Using Event Scheduling
75(10)
List Processing
85(7)
Lists: Basic Properties and Operations
86(1)
Using Arrays for List Processing
87(3)
Using Dynamic Allocation and Linked Lists
90(2)
Advanced Techniques
92(1)
Summary
92(3)
References
93(1)
Exercises
93(2)
Simulation Software
95(58)
History of Simulation Software
96(4)
The Period of Search (1955--60)
97(1)
The Advent (1961--65)
97(1)
The Formative Period (1966--70)
98(1)
The Expansion Period (1971--78)
98(1)
Consolidation and Regeneration (1979--86)
99(1)
The Present Period (1987--present)
99(1)
Selection of Simulation Software
100(4)
An Example Simulation
104(1)
Simulation in C++
104(10)
Simulation in GPSS
114(5)
Simulation in CSIM
119(4)
Simulation Packages
123(6)
Arena
123(1)
AutoMod
124(1)
Deneb/QUEST
125(1)
Extend
126(1)
Micro Saint
127(1)
ProModel
127(1)
Taylor ED
128(1)
WITNESS
128(1)
Experimentation and Statistical Analysis Tools
129(2)
Common Features
129(1)
Analysis Tools
129(2)
Trends in Simulation Software
131(22)
High-Fidelity Simulation
131(1)
Data Exchange Standards
132(1)
The Internet
132(1)
Old Paradigm versus New Paradigm
133(1)
Component Libraries
133(1)
Distributed Manufacturing Simulation/High Level Architeture
133(1)
Embedded Simulation
134(1)
Optimization
134(1)
References
134(2)
Exercises
136(17)
PART TWO: Mathematical and Statistical Models
Statistical Models in Simulation
153(51)
Review of Terminology and Concepts
154(6)
Useful Statistical Models
160(5)
Discrete Distributions
165(5)
Continuous Distributions
170(20)
Poisson Process
190(3)
Empirical Distributions
193(3)
Summary
196(8)
References
196(1)
Exercises
197(7)
Queueing Models
204(51)
Characteristics of Queueing Systems
205(6)
The Calling Population
206(1)
System Capacity
207(1)
The Arrival Process
207(2)
Queue Behavior and Queue Discipline
209(1)
Service Times and the Service Mechanism
209(2)
Queueing Notation
211(1)
Long-Run Measures of Performance of Queueing Systems
212(12)
Time-Average Number in System L
213(2)
Average Time Spent in System per Customer, ω
215(1)
The Conservation Equation: L = λω
216(2)
Server Utilization
218(5)
Costs in Queueing Problems
223(1)
Steady-State Behavior of Infinite-Population Markovian Models
224(15)
Single-Server Queues with Poisson Arrivals and Unlimited Capacity: M/G/1
225(6)
Multiserver Queue: M/M/c/∞/∞
231(6)
Multiserver-Queues with Poisson Arrivals and Limited Capacity: M/M/c/N/∞
237(2)
Steady-State Behavior of Finite-Population Models (M/M/c/K/K)
239(4)
Networks of Queues
243(2)
Summary
245(10)
References
246(1)
Exercises
247(8)
PART THREE: Random Numbers
Random-Number Generation
255(34)
Properties of Random Numbers
255(1)
Generation of Pseudo-Random Numbers
256(2)
Techniques for Generating Random Numbers
258(6)
Linear Congruential Method
258(4)
Combined Linear Congruential Generators
262(2)
Tests for Random Numbers
264(20)
Frequency Tests
266(4)
Runs Tests
270(8)
Tests for Autocorrelation
278(3)
Gap Test
281(2)
Poker Test
283(1)
Summary
284(5)
References
284(1)
Exercises
285(4)
Random-Variate Generation
289(34)
Inverse Transform Technique
290(17)
Exponential Distribution
290(4)
Uniform Distribution
294(1)
Weibull Distribution
294(1)
Triangular Distribution
295(1)
Empirical Continuous Distributions
296(4)
Continuous Distributions without a Closed-Form Inverse
300(1)
Discrete Distributions
301(6)
Direct Transformation for the Normal and Lognormal Distributions
307(2)
Convolution Method
309(1)
Erland Distribution
309(1)
Acceptance-Rejection Technique
310(5)
Poisson Distribution
311(3)
Gamma Distribution
314(1)
Summary
315(8)
References
316(1)
Exercises
316(7)
PART FOUR: Analysis of Simulation Data
Input Modeling
323(44)
Data Collection
324(3)
Indentifying the Distribution with Data
327(9)
Histograms
327(4)
Selecting the Family of Distributions
331(2)
Quantile-Quantile Plots
333(3)
Parameter Estimation
336(7)
Preliminary Statistics: Sample Mean and Sample Variance
336(2)
Suggested Estimators
338(5)
Goodness-of-Fit Tests
343(8)
Chi-Square Test
343(3)
Chi-Square Test with Equal Probabilities
346(2)
Kolmogorov-Smirnov Goodness-of-Fit Test
348(2)
p-Values and ``Best Fits''
350(1)
Selecting Input Models without Data
351(2)
Multivariate and Time-Series Input Models
353(5)
Covariance and Correlation
354(1)
Multivariate Input Models
354(2)
Time-Series Input Models
356(2)
Summary
358(9)
References
359(1)
Exercises
360(7)
Virification and Validation of Simulation Models
367(31)
Model Building, Verification, and Validation
368(1)
Verification of Simulation Models
369(5)
Calibration and Validation of Models
374(19)
Face Validity
376(1)
Validation of Model Assumptions
377(1)
Validating Input-Output Transformations
377(11)
Input-Output Validation: Using Historical Input Data
388(4)
Input-Output Validation: Using a Turing Test
392(1)
Summary
393(5)
References
394(1)
Exercises
395(3)
Output Analysis for a Single Model
398(52)
Types of Simulations with Respect to Output Analysis
399(3)
Stochastic Nature of Output Data
402(5)
Measures of Performance and Their Estimation
407(3)
Point Estimation
407(2)
Interval Estimation
409(1)
Output Analysis for Terminating Simulations
410(8)
Statistical Background
410(1)
Confidence-Interval Estimation for a Fixed Number of Replications
411(3)
Confidence Intervals with Specified Precision
414(2)
Confidence Intervals for Quantiles
416(2)
Output Analysis for Steady-State Simulations
418(23)
Initialization Bias in Steady-State Simulations
419(7)
Statistical Background
426(4)
Replication Method for Steady-State Simulations
430(4)
Sample Size in Steady-State Simulations
434(2)
Batch Means for Interval Estimation in Steady-State Simulations
436(4)
Confidence Intervals for Quantiles
440(1)
Summary
441(9)
References
441(1)
Exercises
442(8)
Comparison and Evaluation of Alternative System Designs
450(52)
Comparison of Two System Designs
451(16)
Independent Sampling with Equal Variances
454(2)
Independent Sampling with Unequal Variances
456(1)
Correlated Sampling, or Common Random Numbers
456(10)
Confidence Intervals with Specified Precision
466(1)
Comparison of Several System Designs
467(9)
Bonferroni Approach to Multiple Comparisons
468(5)
Bonferroni Approach to Selecting the Best
473(3)
Metamodeling
476(9)
Simple Linear Regression
477(4)
Testing for Significance of Regression
481(3)
Multiple Linear Regression
484(1)
Random-Number Assignment for Regression
484(1)
Optimization via Simulation
485(10)
What Does ``Optimization via Simulation'' Mean?
487(1)
Why Is Optimization via Simulation Difficult?
488(1)
Using Robust Heuristics
489(3)
An Illustration: Random Search
492(3)
Summary
495(7)
References
496(1)
Exercises
497(5)
Simulation of Manufacturing and Material Handling Systems
502(26)
Manufacturing and Material Handling Simulations
502(5)
Models of Manufacturing Systems
503(2)
Models of Material Handling
505(1)
Some Common Material Handling Equipment
506(1)
Goals and Performance Measures
507(1)
Issues in Manufacturing and Material Handling Simulations
508(7)
Modeling Downtimes and Failures
508(5)
Trace-Driven Models
513(2)
Case Studies of the Simulation of Manufacturing and Material Handling Systems
515(2)
Summary
517(11)
References
517(1)
Exercises
518(10)
Simulation of Computer Systems
528(43)
Introduction
528(3)
Simulation Tools
531(11)
Process Orientation
533(4)
Event Orientation
537(5)
Model Input
542(11)
Modulated Poisson Process
543(4)
Virtual Memory Referencing
547(6)
High-Level Computer-System Simulation
553(4)
CPU Simulation
557(6)
Memory Simulation
563(3)
Summary
566(5)
References
567(1)
Exercises
568(3)
Appendix Tables571(16)
A.1 Random Digits
572(1)
A.2 Random Normal Numbers
573(1)
A.3 Cumulative Normal Distribution
574(2)
A.4 Cumulative Poisson Distribution
576(4)
A.5 Percentage Points of the Students t Distribution with v Degrees of Freedom
580(1)
A.6 Percentage Points of the Chi-Square Distribution with v Degrees of Freedom
581(1)
A.7 Percentage Points of the F Distribution with α = 0.05
582(1)
A.8 Kolmogorov-Smirnov Critical Values
583(1)
A.9 Maximum-Likelihood Estimates of the Gamma Distribution
584(1)
A.10 Operating-Characteristic Curves for the Two-Sided t-Test for Different Values of Sample Size n
585(1)
A.11 Operating-Characteristic Curves for the One-Sided t-Test for Different Values of Sample Size n
586(1)
Index587

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