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The use of simulation modeling and analysis is becoming increasingly more popular as a technique for improving or investigating process performance. This book is a practical, easy-to-follow reference that offers up-to-date information and step-by-step procedures for conducting simulation studies. It provides sample simulation project support material, including checklists, data-collection forms, and sample simulation project reports and publications to facilitate practitioners' efforts in conducting simulation modeling and analysis projects.

Simulation Modeling Handbook: A Practical Approach has two major advantages over other treatments. First, it is independent of any particular simulation software, allowing readers to use any commercial package or programming language. Second, it was written to insulate practitioners from unnecessary simulation theory that does not focus on their average, practical needs.

As the popularity of simulation studies continues to grow, the planning and execution of these projects, more and more engineering and management professionals will be called upon to perform these tasks. With its simple, no-nonsense approach and focus on application rather than theory, this comprehensive and easy-to-understand guide is the ideal vehicle for acquiring the background and skills needed to undertake effective simulation projects.

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INTRODUCTIONSimulation Modeling and AnalysisOther Types of Simulation ModelsPurposes of SimulationAdvantages to SimulationDisadvantages to SimulationOther ConsiderationsFamous Simulation QuotesBasic Simulation ConceptsAdditional Basic Simulation IssuesSummaryPROBLEM FORMULATIONFormal Problem StatementOrientationProject ObjectivesSummaryPROJECT PLANNINGProject Management ConceptsSimulation Project Manager FunctionsDeveloping the Simulation Project PlanCompressing ProjectsExample Gantt ChartAdvanced Project Management ConceptsProject Management Software PackagesSummarySYSTEM DEFINITIONSystem ClassificationsHigh-Level Flow Chart BasicsComponents and Events to ModelData to Be Included in the ModelOutput DataSummaryINPUT DATA COLLECTION AND ANALYSISSources for Input DataCollecting Input DataDeterministic versus Probabilistic DataDiscrete versus Continuous DataCommon Input Data DistributionsLess Common DistributionsOffset Combination DistributionsAnalyzing Input DataHow Much Data Needs to Be CollectedWhat Happens if I Cannot Fit the Input Data?Software Implementations for Data FittingSummaryMODEL TRANSLATIONSimulation Program SelectionModel Translation Section ContentProgram OrganizationSummaryVERIFICATIONDivide-and-Conquer ApproachAnimationAdvancing the Simulation Clock by Event by EventWriting to an Output FileSummaryVALIDATIONAssumptionsSimplificationsOversightsLimitationsNeed for ValidationTwo Types of ValidationFace ValidityStatistical ValidityValidation Data Analysis ProcessWhen a Model Cannot Be Statistically Validated and            What to Do About ItSummaryEXPERIMENTAL DESIGNFactors and LevelsTwo Alternative Experimental DesignsOne-Factor Experimental DesignsTwo-Factor Experimental DesignsMultifactor Experimental Designs2^k Experimental DesignsExperimental Alternative Factor InteractionsRefining the Experimental AlternativesSummaryANALYSISTerminating System AnalysisNon-Terminating System AnalysisSummaryPROJECT REPORTS AND PRESENTATIONSWritten Report GuidelinesExecutive SummaryEquationsImporting Screen CapturesPresentation GuidelinesPresentation MediaElectronic Presentation Software IssuesActual PresentationSummaryTRAINING SIMULATORSProblem FormulationProject PlanningSystem DefinitionInput Data CollectionModel TranslationVerificationValidationImplementationSummaryEXAMPLESARENA USER'S MINIMANUALSIMULATION USING AUTOMOD AND AUTOSTATSIMPAK USER'S MINIMANUALAPPENDIXES

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