基于鲁棒优化的半期望设施选址与回收物流优化分析

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1、万方数据东北大学硕士学位论文AbstractARobustOptimizationApproachtoSemiobnoxiousFacilit3LocaticProblemandbeml-obnoxlousaClllWLocationProblemandReturnedLogisticsOptimizationAbstractSemi-obnoxiousfacilityprovidesabenefitorservicetosociety,whileadverselyaffectingthequalityoflifeorsocialv

2、aluesinanumberofpossibleways．Asakeylinkinthereturnedlogistics，semi—obnoxiousfacilitylocationproblemhascometobeahotresearcharea．Fromthecomprehensivesurveyofresearchonthesemi—obnoxiousfacilitylocationproblem，mostoftheresearchresultsarebasedonthedeterministicbackground，th

3、einfluenceofuncertaintyonthefacilitylocationisneglected．Thestudyonreturnedlogisticsisfocusedonthenetworkdesign，whichisunderthedeterministicorstochasticenvironment．However,theprobabilitydistributionoftheuncertaindatacan’tbeeasilygotinactualproductioncondition，orsystemca

4、n’tholdtheinfluencefromtheoccurrenceofsmallprobabilityevent．Thustherubostapproachtosemi—facilitylocationproblemandreturnedlogisticsoptimizationhasagreatofacademicandrealisticmeaning．Thispaperapplytherobustoptimizationmethodtosemi-obnoxiousfacilitylocationproblemandretu

5、rnedlogisticsopfimization，detailedresearchcontents，researchmethodsandcorrespondingconclusionsincluding：(1)Withcapacityconstraintandwithoutcapacityconstraint，thispapertakesanoverallconsiderationonaminisumfunctiontorepresentthelocationcostsandanotherminisumfunctiontorepr

6、esenttheobnoxiouseffectsofthefacility,andtwobasicmodelsforsemi—obnoxiousfacilitylocationproblembasedonbi-objectiveparticleswarlTIareestablished．DiscretebinaryparticleswarllloptimizationalgorithmandVC++6．0softwareareappliedtosolvethemodelundercapacityconstraint．(2)Twofo

7、rmsforrecoveryamountuncertaintyareconsideredinthispaperdisposalrateuncertaintyandaveragerecoveryamountuncertainty．Undertheaboveuncertainconditions，intervalanalysisandscenarioanalysisareappliedtodescribe．．III．．万方数据东北大学硕士学位论文Abstragtrecoveryamountuncertainty,semi-obnoxio

8、usfacilitylocationrobustoptimizationmodelsareestablishedbasedonBertsimasrobustoptimizationmethod．Finally,anumericalst