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基于遗传算法的工厂AGV路径优化研究.docx

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基于遗传算法的工厂AGV路径优化研究 Abstract FactoryAGV(AutomatedGuidedVehicle)systemisacrucialaspectofmodernsmartmanufacturing,whichfacilitatesefficientmaterialhandlingoperations.However,optimizingthepathofAGVsinafactorypresentsachallengingComputationalintelligenceproblem.Inthispaper,weproposeanovelapproachusingaGeneticAlgorithm(GA)tooptimizeAGVpathsinafactory.OurresearchaimstodemonstratethatGAapproachescansignificantlyimprovetheefficiencyofAGVrouting,andachieveoptimalsolutionsintermsofdistancetraveledandtimespent.OurexperimentalresultssuggestthatusingGAsignificantlyreducesthedistancetraveled,andthetimetakenbyAGVscomparedtoexistingalgorithms. Keywords:AGV;Pathoptimization;GeneticAlgorithm;Smartmanufacturing Introduction AutomatedGuidedVehicles(AGVs)havegainedwidespreadadoptioninmodernmanufacturingfacilitiesowingtotheirabilitytoaccuratelycarryoutarangeofmaterialhandlingoperationswithinthefacilitywithouttheneedforhumanintervention.AGVscanbeusedtotransportrawmaterials,workpieces,finishedgoods,andtoolingaroundthefacilitytostreamlinematerialhandlingoperations.TheabilityofAGVstooperateautonomouslyhelpstoreducelaborcostsandincreaseoverallefficiencyinmanufacturingfacilities.Asaresult,theoptimalroutingofAGVsinfactorieshasattractedsignificantattentionfromtheresearchcommunity. AGVroutinginfactoriesisacomplextaskthatrequiresoptimizationtominimizethedistancetraveledbyAGVswhilemaximizingefficiency.Thepathoptimizationproblemrequiresconsiderationofseveralvariables,includingthelocationofpick-upanddrop-offpoints,thenumberofvehiclesinthesystem,thecapacityofeachvehicle,andthelayoutofthefactory,amongothers.Traditionaloptimizationalgorithmshavelimitationsinefficientlysolvingtheproblemduetothecomplexityofthetask.Asaresult,theresearchcommunityhasexploredcomputationalintelligenceapproachessuchasSwarmIntelligenceandGeneticAlgorithms(GAs)tooptimizeAGVrouting. GeneticAlgorithms(GAs) GeneticAlgorithms(GAs)arestochasticoptimizationtechniquesthatareinspiredbythebiologicalprocessesofnaturalselectionandgenetics.Th