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1、Computers&OperationsResearch35(2008)2791–2806www.elsevier.com/locate/corAneffectivehybridPSO-basedalgorithmforflowshopschedulingwithlimitedbuffersBoLiua,∗,LingWanga,b,Yi-HuiJinaaDepartmentofAutomation,TsinghuaUniversity,Beijing100084,ChinabSchoolofComputerScience,LiaochengUniver
2、sity,Liaocheng252059,ChinaAvailableonline22January2007AbstractInthispaper,aneffectivehybridalgorithmbasedonparticleswarmoptimization(HPSO)isproposedforpermutationflowshopschedulingproblem(PFSSP)withthelimitedbuffersbetweenconsecutivemachinestominimizethemaximumcompletiontime(i.e
3、.,makespan).First,anovelencodingschemebasedonrandomkeyrepresentationisdeveloped,whichconvertsthecontinu-ouspositionvaluesofparticlesinPSOtojobpermutations.Second,anefficientpopulationinitializationbasedonthefamousNawaz–Enscore–Ham(NEH)heuristicisproposedtogenerateaninitialpopula
4、tionwithcertainqualityanddiversity.Third,alocalsearchstrategybasedonthegeneralizationoftheblockeliminationproperties,namedblock-basedlocalsearch,isproba-bilisticallyappliedtosomegoodparticles.Moreover,simulatedannealing(SA)withmulti-neighborhoodguidedbyanadaptivemeta-Lamarckian
5、learningstrategyisdesignedtopreventtheprematureconvergenceandconcentratecomputingeffortonpromisingsolutions.SimulationresultsandcomparisonsdemonstratetheeffectivenessoftheproposedHPSO.Furthermore,theeffectsofsomeparametersarediscussed.2007ElsevierLtd.Allrightsreserved.Keywords
6、:Flowshopscheduling;Limitedbuffers;Hybridalgorithm;Particleswarmoptimization;Adaptivemeta-Lamarckianlearning;Simulatedannealing1.IntroductionProductionschedulingplaysakeyroleinthemanufacturingsystemsofenterprisesformaintainingacompetitivepositioninfast-changingmarkets,soitisver
7、yimportanttodevelopeffectiveandefficientadvancedmanufacturingandschedulingtechnologiesandapproaches[1].Flowshopschedulingproblem(FSSP)isaclassofwidelystudiedschedulingproblemswithastrongengineeringbackground,whichrepresentsnearlyaquarterofmanufacturingsys-tems,assemblylines,andi
8、nformationservicefacilitiesinusenowadays,andhasearneda
