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基于Memetic算法的RGV动态调度模型.docx

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基于Memetic算法的RGV动态调度模型 Title:AMemeticAlgorithm-basedRGVDynamicSchedulingModel Abstract: Inrecentyears,thedynamicschedulingofRGV(RailGuidedVehicle)systemshasgarneredsignificantattentionduetoitswidespreadapplicationinmanufacturingandlogisticsindustries.TheinherentcomplexityofRGVschedulingproblemsrequiresefficientandeffectiveoptimizationapproachestoensureoptimalsystemperformance.ThispaperproposesaMemeticAlgorithm-basedRGVDynamicSchedulingModel,whichcombinestheadvantagesofgeneticalgorithmsandlocalsearchtechniques.ThismodelaimstoimprovetheproductivityandenergyefficiencyofRGVsystemsbydynamicallyoptimizingtheschedulingofRGVmovements. Keywords:RGVscheduling,dynamicscheduling,memeticalgorithm,optimization,productivity,energyefficiency 1.Introduction RGVsystemshavebecomeincreasinglypopularinmanufacturingandlogisticssettingsduetotheirabilitytoefficientlytransportmaterialsandproducts.However,theschedulingofRGVmovementsisachallengingtask,asitinvolvesmakingreal-timedecisionstocoordinatethemovementsofmultipleRGVsandoptimizevariousobjectivessuchasminimizingthetotalprocessingtimeandenergyconsumption.Toaddressthesechallenges,traditionaloptimizationalgorithmssuchasgeneticalgorithmshavebeenwidelyapplied.However,theyoftensufferfromslowconvergenceandarepronetogettingtrappedinlocaloptima.Therefore,thispaperproposesaMemeticAlgorithm-basedRGVDynamicSchedulingModeltoovercometheselimitations. 2.MemeticAlgorithm-basedRGVDynamicSchedulingModel 2.1ProblemFormulation TheRGVdynamicschedulingproblemcanbeformulatedasfollows:GivenasetofRGVs,tasks,andconveyorbeltsinamanufacturingsystem,determinetheoptimalschedulefortheRGVstoperformthetaskswhileminimizingthetotalprocessingtimeandenergyconsumption. 2.2MemeticAlgorithmFramework TheMemeticAlgorithm-basedRGVDynamicSchedulingModelintegratesgeneticalgorithmsandlocalsearchtechniquestoimprovetheperformanceofRGVscheduling.Themodelconsistsofthefollowingsteps: 1.Encoding:Encodetheschedulingproblemasachromosome,representingthesequencesoftasksperformedbyeachRGV. 2.I