混合型生产过程建模与调度方法分析

《混合型生产过程建模与调度方法分析》由会员上传分享，免费在线阅读，更多相关内容在学术论文-天天文库。

1、^■IModelingandOptimizationAlgorithmforHybridProductionSchedulingABSTRACTShopschedulingisaneffectivewayfortheenterprisestogetmoreprofitandmanagemoreefficiently,whichhasalwaysbeenafocusanddifficultprobleminthefieldofcomputerintegratedmanufacturingsystemsandmanagementscience．Hyb

2、ridproductionprocesshasmanycharacteristicsbetweencontinuousproductionanddiscreteproduction．Itisamulti-objectiveoptimizationproblem，too．Withthedevelopmentofthemarketeconomy,itisbecomingthemainstreamoforganizationalmodeofproductionincreasingly．Theparticleswarmoptimizationalgori

3、thm1Sverysimple，easyandhasafewparameterstoadjust，SOitisveryidealforsettlingmulti。objectiveoptimizationproblems．Thisdissertationexploresmodelingandsolvingmethodsofhybridproductionschedulingproblem．Inthedissertation，thecharacteristicsofhybridproductionprocessweredescribed．Onthe

4、basicofanalysingtheconstraintsoftheproductionschedulingproblemandconsideringmultipleobjectivefunctionsofthescheduling，themodelofhybridproduetionschedulingproblemwasestablished．Multi—objectiveoptimizationproblemwasin仃．oduced．Afterdescribingtheconcepts，avarietyofalgorithmswassu

5、mmarized．Ontllebausicofnleadvantagesanddisadvantagesofthesealgorithms，thetrendswereprospected．ImprovedPSO(ParticleSwarmOptimization)formulti．objectiveoptimizationproblemwasdeveloped．TheexternalpopulationwasretainedSOthatthewh01eparticleswarmavoidedlocaloptimum；andtheglobalopt

6、imumwasupdatedbasedontheprobabilityofindividuals’crowdingdistanceandthelocaloptimumwasupdatedbasedondominancerelationshipbetweeneachparticlelocationanditsoldoptimum，whilethepopulationdiversitywaspreservedwithadaptivedynamicinertiaweightandtheadaptiveadjustmentofinternalpopula

7、tionsize．Thefasterconvergence,extensivedistributionandlesscomputationtimewerevalidatedwiththetestfunctions．Finally,itwasusedformulti-objectivecopperrollproductionschedulingonthebasicofdetailedparticleencoding，decodingandscheduling．Andtheresultofapplicationshowedthatthealgorit

8、hmwaseffective．Keywords：hybridproductionscheduling；Multi-objectiveop