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遗传算法下物流配送中心订单拣选路径优化.docx

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遗传算法下物流配送中心订单拣选路径优化 Introduction Withtherapiddevelopmentofthee-commerceindustry,logisticsandtransportationhavebecomeoneofthemostimportantconcernsforbusinesses.Efficientandcost-effectivelogisticsoperationsareessentialforbusinessestoremaincompetitiveinthemarket.Theorderpickingprocessisacrucialstepinlogisticsoperations,whichinvolvestheselectionofitemsfromstoragelocationstofulfillcustomerorders.Optimizingtheorderpickingprocesscansignificantlyreducecostsandimproveefficiency.Inthisstudy,wewilldiscusstheapplicationofgeneticalgorithminoptimizingorderpickingroutesforlogisticsdistributioncenters. LiteratureReview Orderpickingisoneofthemosttime-consumingandlabor-intensiveactivitiesinawarehouse.Therefore,itiscrucialtooptimizetheorderpickingroutestoimprovetheefficiencyofthelogisticsoperation.However,theorderpickingprocessisacomplextaskduetothelargenumberofitems,differentstoragelocations,andvariousorderrequirements.Researchershaveproposeddifferentmethodstooptimizetheorderpickingprocess,suchasheuristicalgorithms,meta-heuristicalgorithms,andsimulation-basedapproaches. Geneticalgorithm(GA)isameta-heuristicalgorithminspiredbytheprocessofnaturalselection.Ithasbeensuccessfullyappliedinvariousoptimizationproblems,includinglogisticsoptimization.GAisaglobalsearchalgorithmthatcanexplorealargesearchspaceandconvergetotheoptimalsolution.Inrecentyears,GAhasbeenwidelyusedinorderpickingoptimization. Methodology Theorderpickingproblemcanbeformulatedasatravelingsalesmanproblem(TSP),inwhichtheobjectiveistofindtheshortestpaththatvisitsallthestoragelocationsandreturnstothestartingpoint.GAisusedtosolvetheTSPproblembyrepresentingtheorderpickingrouteasachromosome.Thechromosomeisastringofgenesthatrepresentstheorderinwhichthestoragelocationsarevisited.Thefitnessfunctionevaluatesthequalityofthechromosome,whichisdefinedasthetotaldistancetraveledbytheorderpicker.TheGAalgorithmiterativelygeneratesapopulationofchromosomes,selectsthefittestchromosomes,andusesgeneticoperatorssuchascrossoverandmutationtogeneratea