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1、2010屆本科畢業(yè)設(shè)計(jì)(論文)題目:基于粒子群算法的庫(kù)存——路徑問題研究學(xué)院:經(jīng)濟(jì)與管理學(xué)院專業(yè):工業(yè)工程班級(jí):0601班姓名:許潔指導(dǎo)教師:吳斌起訖日期:2010.04-2010.06南京工業(yè)大學(xué)本科生畢業(yè)設(shè)計(jì)(論文)基于粒子群算法的庫(kù)存——路徑問題研究摘要庫(kù)存——路徑問題(InventoryRoutingProblem,IRP)是供應(yīng)商庫(kù)存管理(VMI)模式下的核心問題,通過協(xié)調(diào)庫(kù)存控制與運(yùn)輸調(diào)度,使庫(kù)存和運(yùn)輸?shù)染C合物流成本最低。本文深入分析IRP問題國(guó)內(nèi)外研究現(xiàn)狀,對(duì)IRP問題的分類、各種建模方法及求解方法進(jìn)行總結(jié)。在此基礎(chǔ)上研究了多周期確定需求下的庫(kù)存路徑問題,建立了混合整
2、數(shù)規(guī)劃模型。研究了粒子群算法對(duì)該模型的優(yōu)化求解;基于整數(shù)編碼方法,使用隨機(jī)初始化的方法產(chǎn)生初始解,為了提高算法的性能,引入四種慣性權(quán)重調(diào)整策略和兩種學(xué)習(xí)因子調(diào)整策略?;贛atlab編程進(jìn)行實(shí)驗(yàn)仿真,使用離線性能和在線性能對(duì)算法進(jìn)行評(píng)價(jià)。討論了算法的迭代次數(shù)、慣性權(quán)重調(diào)整策略等參數(shù)對(duì)算法性能的影響,找出了解決該類問題的適合參數(shù)。并與遺傳算法、經(jīng)濟(jì)訂貨批量法的優(yōu)化結(jié)果進(jìn)行了比較,結(jié)果表明粒子群算法是求解IRP問題的有效算法。關(guān)鍵詞:庫(kù)存路徑問題,粒子群算法,數(shù)學(xué)建模,Matlab編程Ⅰ摘要ResearchonInventoryRoutingProblemBasedonParticle
3、SwarmOptimizationAlgorithmABSTRACTInventoryroutingproblems(IRP)arecoreissuesofVendorManagedInventory(VMI),whichaimstominimizetheintegrativecostofinventoryandtransportationthroughcoordinatinginventorycontrolandtransportationplans.ThispaperfirstlysurveysthecurrentresearchofIRPathomeandabroad,the
4、nsummarizesandclassifiesallkindsofIRPmodelandoptimizationalgorithm.Next,akindofmulti-periodinventoryroutingproblemwithdeterminateneedisstudied,andamixedintegerprogrammingmodelbasedontheproblemisbuilt.Particleswarmoptimization(PSO)algorithmisproposedtooptimizethemodel.Thesolutionisencodedininte
5、gerandisinitializedrandomly.InordertoimprovetheperformanceofPSO,fourkindsofstrategieswhichareusedtoadjusttheparameterofinertiaweightofthePSOalgorithmandtwokindsoflearningstrategiesareincorporatedintothealgorithm.ThealgorithmisimplementedinMatlab,anditisevaluatedbytwocriterions:on-lineperforman
6、ceandoff-lineperformance.Theeffectofdifferentparametervaluesaboutiteration,inertiaweightandsoonarediscussedintheexperience,andtheappropriateparametersarefound.Acomparisonwiththetraditionaleconomicorderquantityandgeneticalgorithmsshowsthattheparticleswarmoptimizationoutperformsothersandiseffect
7、iveforsolvingIRP.KeyWords:Inventory-RoutingProblem;ParticleSwarmOptimization;MathematicalModel;MatlabProgrammingⅡ目錄目錄摘要..............................................................ⅠABSTRACT..............................................