Discrete-Event System Simulation - GBV

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Discrete-Event System SimulationFIFTH EDITIONJerry BanksTechnolögico de Monterrey, Campus MonterreyJohn S. Carson IIIndependent Simulation ConsultantBarry L. NelsonNorthwesternUniversityDavid M. NicolUniversity of Illinois, Urbana-ChampaignUpper Saddle River Boston Columbus San Francisco New York AmsterdamCape Town Dubai London Madrid Milan Munich Paris Montreal TorontoDelhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo

ContentsPreface11What's New in the Fifth Edition15List of Materials Available on www.bcnn.net16About the 1222.1Introduction to Discrete-Event System SimulationIntroduction to SimulationWhen Simulation Is the Appropriate ToolWhen Simulation Is Not AppropriateAdvantages and Disadvantages of SimulationAreas of ApplicationSome Recent Applications of SimulationSystems and System EnvironmentComponents of a SystemDiscrete and Continuous SystemsModel of a SystemTypes of ModelsDiscrete-Event System SimulationSteps in a Simulation StudyReferencesExercisesSimulation Examples in a SpreadsheetThe Basics of Spreadsheet Simulation2.1.1 How to Simulate Randomness212222232527303032333334343940424343

42.22.32.42.52.633.13.23.344.1Contents2.1.2 The Random Generators Used in the Examples2.1.3 How to Use the Spreadsheets2.1.4 How to Simulate a Coin Toss2.1.5 How to Simulate a Random Service Time2.1.6 How to Simulate a Random Arrival Time2.1.7 A Framework for Spreadsheet SimulationA Coin Tossing GameQueueing Simulation in a Spreadsheet2.3.1 Waiting Line Models2.3.2 Simulating a Single-Server Queue2.3.3 Simulating a Queue with Two ServersInventory Simulation in a Spreadsheet2.4.1 Simulating the News Dealer's Problem2.4.2 Simulating an (M,N) Inventory PolicyOther Examples of Simulation2.5.1 Simulation of a Reliability Problem2.5.2 Simulation of Hitting a Target2.5.3 Estimating the Distribution of Lead-Time Demand2.5.4 Simulating an Activity NetworkSummaryReferencesExercisesGeneral PrinciplesConcepts in Discrete-Event Simulation3.1.1 The Event Scheduling/Time Advance Algorithm3.1.2 WorldViews3.1.3 Manual Simulation Using Event SchedulingList Processing3.2.1 Basic Properties and Operations Performed on Lists3.2.2 Using Arrays for List Processing3.2.3 Using Dynamic Allocation and Linked Lists3.2.4 Advanced TechniquesSummaryReferencesExercisesSimulation SoftwareHistory of Simulation Software4.1.1 The Period of Search (1955-1960)4.1.2 The Advent (1961-1965)4.1.3 The Formative Period (1966-1970)4.1.4 The Expansion Period 137137137138

5Contents4.24.34.44.54.64.74.8II55.15.25.34.1.5 The Period of Consolidation and Regeneration (1979-1986)4.1.6 The Period of Integrated Environments (1987-2008)4.1.7 The Future (2009-2011)Selection of Simulation SoftwareAn Example SimulationSimulation in JavaSimulation in GPSSSimulation in SSFSimulation Environments4.7.1 AnyLogic4.7.2 Arena4.7.3 AutoMod4.7.4 Enterprise Dynamics4.7.5 ExtendSim4.7.6 Flexsim4.7.7 ProModel4.7.8 SIMUL8Experimentation and Statistical-Analysis Tools4.8.1 Common Features4.8.2 ProductsReferencesExercisesMathematical and Statistical ModelsStatistical Models in SimulationReview of Terminology and Concepts5.1.1 Di screte random variables5.1.2 Continuous random variables5.1.3 Cumulative distribution function5.1.4 Expectation5.1.5 The modeUseful Statistical Models5.2.1 Queueing systems5.2.2 Inventory and supply-chain systems5.2.3 Reliability and maintainability5.2.4 Limited data5.2.5 Other distributionsDiscrete Distributions5.3.1 Bernoulli trials and the Bernoulli distribution5.3.2 Binomial distribution5.3.3 Geometric and Negative Binomial distributions5.3.4 Poisson 97197197200201201201201201202204205

inuous Distributions5.4.1 Uniform distribution5.4.2 Exponential distribution5.4.3 Gamma distribution5.4.4 Erlang distribution5.4.5 Normal distribution5.4.6 Weibull distribution5.4.7 Triangular distribution5.4.8 Lognormal distribution5.4.9 Beta distributionPoisson Process5.5.1 Properties of a Poisson Process5.5.2 Nonstationary Poisson ProcessEmpirical DistributionsSummaryReferencesExercisesQueueing 6237237245Characteristics of Queueing Systems2466.1.1 The Calling Population2476.1.2 System Capacity2406.1.3 The Arrival Process2486.1.4 Queue Behavior and Queue Discipline2506.1.5 Service Times and the Service Mechanism250Queueing Notation252Long-Run Measures of Performance of Queueing Systems2536.3.1 Time-Average Number in System L2536.3.2 Average Time Spent in System Per Customer w2556.3.3 The Conservation Equation: L Xw2576.3.4 Server Utilization2586.3.5 Costs in Queueing Problems268Steady-State Behavior of Infinite-Population Markovian Models2656.4.1 Single-Server Queues with Poisson Arrivals and Unlimited Capacity: M/G/l2666.4.2 Multiserver Queue: M/M/c/oo/oo2716.4.3 Multiserver Queues with Poisson Arrivals and Limited Capacity: M/M/c/N /00П6Steady-State Behavior of Finite-Population Models {M/M/c/K/K)278Networks of Queues281Rough-cut Modeling: An Illustration283Summary285References286Exercises286

7ContentsIII77.17.27.37.47.588.18.28.38.4Random NumbersRandom-Number GenerationProperties of Random NumbersGeneration of Pseudo-Random NumbersTechniques for Generating Random Numbers7.3.1 Linear Congruential Method7.3.2 Combined Linear Congruential Generators7.3.3 Random-Number StreamsTests for Random Numbers7.4.1 Frequency Tests7.4.2 Tests for riate GenerationInverse-Transform Technique8.1.1 Exponential Distribution8.1.2 Uniform Distribution8.1.3 WeibuII Distribution8.1.4 Triangular Distribution8.1.5 Empirical Continuous Distributions8.1.6 Continuous Distributions without a Closed-Form Inverse8.1.7 Discrete DistributionsAcceptance-Rejection Technique8.2.1 Poisson Distribution8.2.2 Nonstationary Poisson Process8.2.3 Gamma DistributionSpecial Properties8.3.1 Direct Transformation for the Normal and Lognormal Distributions8.3.2 Convolution Method8.3.3 More Special 29330332334339340341342343345345345346IV Analysis of Simulation Data35193539.19.2Input ModelingData CollectionIdentifying the Distribution with Data9.2.1 Histograms354359359

Contents9.39.49.59.69.79.8109.2.2 Selecting the Family of Distributions9.2.3 Quantile-Quantile PlotsParameter Estimation9.3.1 Preliminary Statistics: Sample Mean and Sample Variance9.3.2 Suggested EstimatorsGoodness-of-Fit Tests9.4.1 Chi-Square Test9.4.2 Chi-Square Test with Equal Probabilities9.4.3 Kolmogorov-Smirnov Goodness-of-Fit Test9.4.4 p-Values and "Best Fits"Fitting a Nonstationary Poisson ProcessSelecting Input Models without DataMultivariate and Time-Series Input Models9.7.1 Covariance and Correlation9.7.2 Multivariate Input Models9.7.3 Time-Series Input Models9.7.4 The Normal-to-Anything on, Calibration, and Validation of Simulation 839039239439639740610.1 Model Building, Verification, and Validation10.2 Verification of Simulation Models10.3 Calibration and Validation of Models10.3.1 Face Validity10.3.2 Validation of Model Assumptions10.3.3 Validating Input-Output Transformations10.3.4 Input-Output Validation: Using Historical Input Data10.3.5 Input-Output Validation: Using a Turing Test10.4 043143243311435Estimation of Absolute Performance11.1 Types of Simulations with Respect to Output Analysis11.2 Stochastic Nature of Output Data11.3 Absolute Measures of Performance and Their Estimation11.3.1 Point Estimation11.3.2 Confidence-Interval Estimation11.4 Output Analysis for Terminating Simulations11.4.1 Statistical Background11.4.2 Confidence Intervals with Specified Precision11.4.3 Quantiles436439441441443445445449451

9Contents11.511.61212.112.212.312.412.511.4.4 Estimating Probabilities and Quantiles from Summary DataOutput Analysis for Steady-State Simulations11.5.1 Initialization Bias in Steady-State Simulations11.5.2 Error Estimation for Steady-State Simulation11.5.3 Replication Method for Steady-State Simulations11.5.4 Sample Size in Steady-State Simulations11.5.5 Batch Means Method for Steady-State Simulations11.5.6 Steady-State QuantilesSummaryReferencesExercisesEstimation of Relative PerformanceComparison of Two System Designs12.1.1 Independent Sampling12.1.2 Common Random Numbers (CRN)12.1.3 Confidence Intervals with Specified PrecisionComparison of Several System Designs12.2.1 Bonferroni Approach to Multiple Comparisons12.2.2 Selection of the BestMetamodeling12.3.1 Simple Linear Regression12.3.2 Metamodeling and Computer SimulationOptimization via Simulation12.4.1 What Does "Optimization via Simulation" Mean?12.4.2 Why is Optimization via Simulation Difficult?12.4.3 Using Robust Heuristics12.4.4 An Illustration: Random 9510512513516518519520V Applications5251352713.113.213.3Simulation of Manufacturing and Material-Handling SystemsManufacturing and Material-Handling Simulations13.1.1 Models of Manufacturing Systems13.1.2 Models of Material Handling Systems13.1.3 Some Common Material-Handling EquipmentGoals and Performance MeasuresIssues in Manufacturing and Material-Handling Simulations13.3.1 Modeling Downtimes and Failures13.3.2 Trace-Driven Models528528530530532533533537

1013.413.513.6ContentsCase Studies of the Simulation of Manufacturing and Material HandlingManufacturing Example: An Assembly-line Simulation13.5.1 System Description and Model Assumptions13.5.2 Presimulation Analysis13.5.3 Simulation Model and Analysis of the Designed System13.5.4 Analysis of Station Utilization13.5.5 Analysis of Potential System Improvements13.5.6 Concluding Words: The Gizmo Assembly-Line 14.414.514.614.714.814.914.1014.11Simulation of Networked Computer SystemsIntroductionSimulation Tools14.2.1 Process Orientation14.2.2 Event OrientationModel Input14.3.1 Modulated Poisson Process14.3.2 Poisson-Pareto Process14.3.3 Pareto-length Phase Time14.3.4 WWW TrafficMobility Models in Wireless SystemsThe OSI Stack ModelPhysical Layer in Wireless Systems14.6.1 Propagation Models14.6.2 Determining the ReceiversMedia Access Control14.7.1 Token-Passing Protocols14.7.2 EthernetData Link LayerTCPModel Construction14.10.1 Construction14.10.2 DML ix609Index625

I Introduction to Discrete-Event System Simulation 19 1 Introduction to Simulation 21 1.1 When Simulation Is the Appropriate Tool 22 1.2 When Simulation Is Not Appropriate 22 1.3 Advantages and Disadvantages of Simulation 23 1.4 Areas of Application 25 1.5 Some Recent Applications of Simulation

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