Discrete-Event System Simulation PDF
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Discrete-Event System Simulation Banks Carson II Nelson Nicol Fifth Edition ISBN 10: 1-292-02437-2 ISBN 13: 978-1-292-02437-0 Pearson Education Limited Edinburgh Gate Harlow Essex CM20 2JE England and Associated Companies throughout the world Visit us on the World Wide Web at: www.pearsoned.co.uk © Pearson Education Limited 2014 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without either the prior written permission of the publisher or a licence permitting restricted copying in the United Kingdom issued by the Copyright Licensing Agency Ltd, Saffron House, 6–10 Kirby Street, London EC1N 8TS. All trademarks used herein are the property of their respective owners. The use of any trademark in this text does not vest in the author or publisher any trademark ownership rights in such trademarks, nor does the use of such trademarks imply any affi liation with or endorsement of this book by such owners. ISBN 10: 1-292-02437-2 ISBN 13: 978-1-292-02437-0 British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Printed in the United States of America 11222334452817279381601917571551539 P E A R S O N C U S T O M L I B R AR Y Table of Contents 1. Introduction to Simulation Jerry Banks/John S. Carson II/Barry L. Nelson/David M. Nicol 1 2. Simulation Examples in a Spreadsheet Jerry Banks/John S. Carson II/Barry L. Nelson/David M. Nicol 23 3. General Principles Jerry Banks/John S. Carson II/Barry L. Nelson/David M. Nicol 89 4. Simulation Software Jerry Banks/John S. Carson II/Barry L. Nelson/David M. Nicol 119 5. Statistical Models in Simulation Jerry Banks/John S. Carson II/Barry L. Nelson/David M. Nicol 171 6. Queueing Models Jerry Banks/John S. Carson II/Barry L. Nelson/David M. Nicol 227 7. Random-Number Generation Jerry Banks/John S. Carson II/Barry L. Nelson/David M. Nicol 275 8. Random-Variate Generation Jerry Banks/John S. Carson II/Barry L. Nelson/David M. Nicol 297 9. Input Modeling Jerry Banks/John S. Carson II/Barry L. Nelson/David M. Nicol 331 10. Verification, Calibration, and Validation of Simulation Models Jerry Banks/John S. Carson II/Barry L. Nelson/David M. Nicol 385 11. Estimation of Absolute Performance Jerry Banks/John S. Carson II/Barry L. Nelson/David M. Nicol 415 12. Estimation of Relative Performance Jerry Banks/John S. Carson II/Barry L. Nelson/David M. Nicol 461 13. Simulation of Manufacturing and Material-Handling Systems Jerry Banks/John S. Carson II/Barry L. Nelson/David M. Nicol 505 I 553573 Appendix Jerry Banks/John S. Carson II/Barry L. Nelson/David M. Nicol 537 Index 553 II Introduction to Simulation Asimulationistheimitationoftheoperationofareal-worldprocessorsystemovertime.Whether done by hand or on a computer, simulation involves the generation of an artificial history of a system and the observation of that artificial history to draw inferences concerning the operating characteristicsoftherealsystem. Thebehaviorofasystemasitevolvesovertimeisstudiedbydevelopingasimulationmodel. Thismodelusuallytakestheformofasetofassumptionsconcerningtheoperationofthesystem. Theseassumptionsareexpressedinmathematical,logical,andsymbolicrelationshipsbetweenthe entities,orobjectsofinterest,ofthesystem.Oncedevelopedandvalidated,amodelcanbeusedto investigateawidevarietyof“whatif”questionsaboutthereal-worldsystem.Potentialchangesto thesystemcanfirstbesimulated,inordertopredicttheirimpactonsystemperformance.Simulation canalsobeusedtostudysystemsinthedesignstage,beforesuchsystemsarebuilt.Thus,simulation modelingcanbeusedbothasananalysistoolforpredictingtheeffectofchangestoexistingsystems andasadesigntooltopredicttheperformanceofnewsystemsundervaryingsetsofcircumstances. Insomeinstances,amodelcanbedevelopedwhichissimpleenoughtobe“solved”bymathe- maticalmethods.Suchsolutionsmightbefoundbytheuseofdifferentialcalculus,probabilitytheory, algebraicmethods,orothermathematicaltechniques.Thesolutionusuallyconsistsofoneormore numerical parameters, which are called measures of performance of the system. However, many real-world systems are so complex that models of these systems are virtually impossible to solve mathematically.Intheseinstances,numerical,computer-basedsimulationcanbeusedtoimitatethe FromChapter1ofDiscrete-EventSystemSimulation,FifthEdition.JerryBanks,JohnS.CarsonII, BarryL.Nelson,DavidM.Nicol.Copyright c 2010byPearsonEducation,Inc. (cid:2) PublishedbyPearsonPrenticeHall.Allrightsreserved. 1 IntroductiontoSimulation behaviorofthesystemovertime.Fromthesimulation,dataarecollectedasifarealsystemwere beingobserved.Thissimulation-generateddataisusedtoestimatethemeasuresofperformanceof thesystem. Thischapterinitiallydiscusseswhentousesimulation,itsadvantagesanddisadvantages,and actual areas of its application. Then the concepts of system and model are explored. Finally, an outlineisgivenofthestepsinbuildingandusingasimulationmodelofasystem. 1 WhenSimulationIstheAppropriateTool The availability of special-purpose simulation languages, of massive computing capabilities at a decreasingcostperoperation,andofadvancesinsimulationmethodologieshavemadesimulation oneofthemostwidelyusedandacceptedtoolsinoperationsresearchandsystemsanalysis.Circum- stancesunderwhichsimulationistheappropriatetooltousehavebeendiscussedbymanyauthors, fromNayloretal.[1966]toShannon[1998].Simulationcanbeusedforthefollowingpurposes: 1. Simulation enables the study of, and experimentation with, the internal interactions of a complexsystemorofasubsystemwithinacomplexsystem. 2. Informational,organizational,andenvironmentalchangescanbesimulated,andtheeffect ofthesealterationsonthemodel’sbehaviorcanbeobserved. 3. Theknowledgegainedduringthedesigningofasimulationmodelcouldbeofgreatvalue towardsuggestingimprovementinthesystemunderinvestigation. 4. Changing simulation inputs and observing the resulting outputs can produce valuable insightsaboutwhichvariablesarethemostimportantandhowvariablesinteract. 5. Simulationcanbeusedasapedagogicaldevicetoreinforceanalyticsolutionmethodologies. 6. Simulationcanbeusedtoexperimentwithnewdesignsorpoliciesbeforeimplementation, soastoprepareforwhatmighthappen. 7. Simulationcanbeusedtoverifyanalyticsolutions. 8. Simulatingdifferentcapabilitiesforamachinecanhelpdetermineitsrequirements. 9. Simulationmodelsdesignedfortrainingmakelearningpossible,withoutthecostanddisrup- tionofon-the-jobinstruction. 10. Animationcanshowasysteminsimulatedoperationsothattheplancanbevisualized. 11. Amodernsystem(factory,waferfabricationplant,serviceorganization,etc.)issocomplex thatitsinternalinteractionscanbetreatedonlythroughsimulation. 2 WhenSimulationIsNotAppropriate ThissectionisbasedonanarticlebyBanksandGibson[1997],whichgivestenrulesforevaluating whensimulationisnotappropriate.Thefirstruleindicatesthatsimulationshouldnotbeusedwhen the problem can be solved by common sense.An example is given of an automobile tag facility servingcustomerswhoarriverandomlyatanaveragerateof100/hourandareservedatameanrate 2 IntroductiontoSimulation of12/hour.Todeterminetheminimumnumberofserversneeded,simulationisnotnecessary.Just compute100/12 8.33,whichindicatesthatnineormoreserversareneeded. = Thesecondrulesaysthatsimulationshouldnotbeusediftheproblemcanbesolvedanalytically. Forexample,undercertainconditions,theaveragewaitingtimeintheexampleabovecanbefound usingthetoolsavailableatwww.bcnn.net. The next rule says that simulation should not be used if it is less expensive to perform direct experiments.Theexampleofafast-fooddrive-inrestaurantisgiven,whereitwaslessexpensiveto stageapersontakingordersusingahand-heldterminalandvoicecommunicationtodeterminethe effectofaddinganotherorderstationoncustomerwaitingtime. Thefourthrulesaysnottousesimulationifthecostsexceedthesavings.Therearemanysteps incompletingasimulation,aswillbediscussedinSection12,andthesemustbedonethoroughly. If a simulation study costs $20,000 and the savings might be $10,000, simulation would not be appropriate. Rulesfiveandsixindicatethatsimulationshouldnotbeperformediftheresourcesortimeare notavailable.Ifthesimulationisestimatedtocost$20,000andthereisonly$10,000available,the suggestionisnottoventureintoasimulationstudy.Similarly,ifadecisionisneededintwoweeks andasimulationwouldtakeamonth,thesimulationstudyisnotadvised. Simulationtakesdata,sometimeslotsofdata.Ifnodataisavailable,notevenestimates,simu- lationisnotadvised.Thenextruleconcernstheabilitytoverifyandvalidatethemodel.Ifthereis notenoughtimeorifthepersonnelarenotavailable,simulationisnotappropriate. Ifmanagershaveunreasonableexpectations,iftheyaskfortoomuchtoosoon,orifthepower ofsimulationisoverestimated,simulationmightnotbeappropriate. Last, if system behavior is too complex or cannot be defined, simulation is not appropriate. Humanbehaviorissometimesextremelycomplextomodel. 3 AdvantagesandDisadvantagesofSimulation Simulation is intuitively appealing to a client because it mimics what happens in a real system or whatisperceivedforasystemthatisinthedesignstage.Theoutputdatafromasimulationshould directly correspond to the outputs that could be recorded from the real system.Additionally, it is possibletodevelopasimulationmodelofasystemwithoutdubiousassumptions(suchasthesame statisticaldistributionforeveryrandomvariable)ofmathematicallysolvablemodels.Fortheseand otherreasons,simulationisfrequentlythetechniqueofchoiceinproblemsolving. In contrast to optimization models, simulation models are “run” rather than solved. Given a particular set of input and model characteristics, the model is run and the simulated behavior is observed.Thisprocessofchanginginputsandmodelcharacteristicsresultsinasetofscenariosthat areevaluated.Agoodsolution,eitherintheanalysisofanexistingsystemorinthedesignofanew system,isthenrecommendedforimplementation. Simulationhasmanyadvantages,butsomedisadvantages.ThesearelistedbyPegden,Shannon, andSadowski[1995].Someadvantagesarethese: 1. Newpolicies,operatingprocedures,decisionrules,informationflows,organizationalproce- dures,andsooncanbeexploredwithoutdisruptingongoingoperationsoftherealsystem. 3 IntroductiontoSimulation 2. New hardware designs, physical layouts, transportation systems, and so on can be tested withoutcommittingresourcesfortheiracquisition. 3. Hypothesesabouthoworwhycertainphenomenaoccurcanbetestedforfeasibility. 4. Timecanbecompressedorexpandedtoallowforaspeed-uporslow-downofthephenomena underinvestigation. 5. Insightcanbeobtainedabouttheinteractionofvariables. 6. Insightcanbeobtainedabouttheimportanceofvariablestotheperformanceofthesystem. 7. Bottleneckanalysiscanbeperformedtodiscoverwhereworkinprocess,information,ma- terials,andsoonarebeingdelayedexcessively. 8. A simulation study can help in understanding how the system operates rather than how individualsthinkthesystemoperates. 9. “Whatif”questionscanbeanswered.Thisisparticularlyusefulinthedesignofnewsystems. Somedisadvantagesarethese: 1. Model building requires special training. It is an art that is learned over time and through experience.Furthermore,iftwomodelsareconstructedbydifferentcompetentindividuals, theymighthavesimilarities,butitishighlyunlikelythattheywillbethesame. 2. Simulationresultscanbedifficulttointerpret.Mostsimulationoutputsareessentiallyrandom variables(theyareusuallybasedonrandominputs),soitcanbehardtodistinguishwhether anobservationistheresultofsysteminterrelationshipsorofrandomness. 3. Simulation modeling and analysis can be time consuming and expensive. Skimping on resources for modeling and analysis could result in a simulation model or analysis that isnotsufficienttothetask. 4. Simulationisusedinsomecaseswhenananalyticalsolutionispossible,orevenpreferable, as was discussed in Section 2. This might be particularly true in the simulation of some waitinglineswhereclosed-formqueueingmodelsareavailable. Indefenseofsimulation,thesefourdisadvantages,respectively,canbeoffsetasfollows: 1. Vendorsofsimulationsoftwarehavebeenactivelydevelopingpackagesthatcontainmodels thatneedonlyinputdatafortheiroperation.Suchmodelshavethegenerictag“simulator” or“template.” 2. Manysimulationsoftwarevendorshavedevelopedoutput-analysiscapabilitieswithintheir packagesforperformingverythoroughanalyses. 3. Simulationcanbeperformedfastertodaythanyesterdayandwillbeevenfastertomorrow, because of advances in hardware that permit rapid running of scenarios and because of advances in many simulation packages. For example, some simulation software contains constructs for modeling material handling that uses such transporters as fork-lift trucks, conveyors,andautomatedguidedvehicles. 4. Closed-formmodelsarenotabletoanalyzemostofthecomplexsystemsthatareencountered in practice. During the many years of consulting practice by two of the authors, not one problemwasencounteredthatcouldhavebeensolvedbyaclosed-formsolution. 4 IntroductiontoSimulation 4 AreasofApplication Theapplicationsofsimulationarevast.TheWinterSimulationConference(WSC)isanexcellent waytolearnmoreaboutthelatestinsimulationapplicationsandtheory.Therearealsonumerous tutorialsatboththebeginningandadvancedlevels.WSCissponsoredbysixtechnicalsocietiesand theNationalInstituteofStandardsandTechnology(NIST).ThetechnicalsocietiesaretheAmerican StatisticalAssociation(ASA),theAssociationforComputingMachinery/SpecialInterestGroupon Simulation (ACM/SIGSIM), the Institute of Electrical and Electronics Engineers: Systems, Man andCyberneticsSociety(IEEE/SMCS),theInstituteofIndustrialEngineers(IIE),theInstitutefor OperationsResearchandtheManagementSciences:SimulationSociety(INFORMS-SIM),andthe Society for Modeling and Simulation International (SCS). Information about the upcomingWSC canbeobtainedfromwww.wintersim.org.WSCprogramswithfullpapersareavailablefrom www.informs-cs.org/wscpapers.html.Somepresentations,byarea,fromarecentWSC arelistednext: ManufacturingApplications MethodologyforSelectingtheMostSuitableBottleneckDetectionMethod AutomatingtheDevelopmentofShipyardManufacturingModels EmulationinManufacturingEngineeringProcesses OptimizedMaintenanceDesignforManufacturingPerformanceImprovement ProductivityManagementinanAutomotive-PartsIndustry ManufacturingLineDesignsinJapaneseAutomobileManufacturingPlants WaferFabrication AParadigmShiftinAssigningLotstoTools SchedulingaMulti-ChipPackageAssemblyLinewithReentrantProcesses ExecutionLevelCapacityAllocationDecisionsforAssembly—TestFacilities ManagingWIPandCycleTimewiththeHelpofLoopControl BusinessProcessing ANewPolicyfortheServiceRequestAssignmentProblem ProcessExecutionMonitoringandAdjustmentSchemes In-StoreMerchandizingofRetailStores SalesForecastingforRetailSmallStores ConstructionEngineeringandProjectManagement SchedulingofLimitedBar-BendersoverMultipleBuildingSites ConstructingRepetitiveProjects TrafficOperationsforImprovedPlanningofRoadConstructionProjects TemplateforModelingTunnelShaftConstruction DecisionSupportToolforPlanningTunnelConstruction Logistics,Transportation,andDistribution OperatingPoliciesforaBargeTransportationSystem DispensingPlanforEmergencyMedicalSuppliesintheEventofBioterrorism 5
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