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用遗传算法解决车辆优化调度问题.doc

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用遗传算法解决车辆优化调度问题.doc

I摘要近年来,物流作为第三方利润的源泉受到国内各行业的极大重视并得到了较大的发展。在高度发展的商业社会中,传统的VSP算法已无法满足顾客需求对物流配送提出的要求,于是时间窗的概念应运而生。带有时间窗的车辆优化调度问题是比VSP复杂程度更高的NP难题。本文在研究物流配送车辆优化调度问题的基础上,对有时间窗的车辆优化调度问题进行了分析。并对所采用的遗传算法的基本理论做了论述。对于有时间窗的非满载VSP问题,将货运量约束和软时间窗约束转化为目标约束,建立了非满载VSP模型,设计了基于自然数编码,使用最大保留交叉、改进的反转变异等技术的遗传算法。经实验分析,取得了较好的结果。由于此问题为小组成员共同研究,本文重点论述了本人完成的关于适应度函数和变异操作的部分。关键词物流配送车辆优化调度遗传算法时间窗IIAbstractRecentyears,logistics,takenasthirdprofitresource,hasbeendevelopingrapidly.Inthedevelopedcommercialsociety,traditionalVSPalgorithmhavebeenunabletomeettherequirementthatQuickResponsetocustomerdemandhadbroughtforth,thentheconceptionofTimeWindowhascomeintobeing.ThevehicleschedulingproblemwithtimewindowisalsoaNPhardproblembeingmorecomplicatedthanVSP.Thistexthasbeenresearchedtothevehicleschedulingproblemwithtimewindowonthebasisofresearchedtologisticvehicleschedulingproblem.Andithasexplainedthebasictheoryofgeneticalgorithm.OntheVSPwithtimewindow,whiletherestraintsofcapacityandtimewindowsarechangedintoobjectrestraints,amathematicmodelisestablished.Weusetechniquesuchasmaximumpreservedcrossoveranddesigngeneticalgorithmonnaturenumber,whichcandealwithsofttimewindowsthroughexperimentalanalysis,havemadebetterresult.Becausethisproblemwasstudiedtogetherforgroupmembers,thistexthasexpoundedthepartaboutfitnessfunctionandmutationoperatorthatIfinished.KeywordslogisticdistributionvehicleschedulingproblemgeneticalgorithmtimewindowsIII目录摘要...........................................................................................................IAbstract......................................................................................................II目录.........................................................................................................III引言...........................................................................................................1第1章概述............................................................................................21.1研究背景.....................................................................................21.2物流配送车辆优化调度的研究动态和水平....................................41.2.1问题的提出.......................................................................41.2.2分类..................................................................................51.2.3基本问题与基本方法.........................................................61.2.4算法..................................................................................61.2.5货运车辆优化调度问题的分类...........................................81.3研究的意义..................................................................................91.4研究的范围................................................................................10第2章有时间窗的车辆优化调度问题VSPTW.......................................112.1时间窗的定义............................................................................112.2VSPTW问题的结构......................................................................13第3章遗传算法基本理论.......................................................................143.1遗传算法的基本原理.................................................................143.1.1遗传算法的特点..............................................................143.1.2遗传算法的基本步骤和处理流程.....................................153.1.3遗传算法的应用..............................................................163.2编码..........................................................................................173.2.1二进制编码.....................................................................183.2.2Gray编码........................................................................183.2.3实数向量编码.................................................................183.2.4排列编码........................................................................19IV3.3适应度函数................................................................................193.3.1目标函数映射成适应度函数............................................193.3.2适应度定标.....................................................................203.4遗传算法的基因操作.................................................................213.4.1选择算子........................................................................213.4.2交叉算子........................................................................223.4.3变异算子........................................................................253.5遗传算法控制参数设定..............................................................28第4章遗传算法求解有时间窗非满载VSP..............................................304.1问题描述...................................................................................304.2数学模型...................................................................................314.2.1一般VSP模型.................................................................314.2.2有时间窗VSP模型..........................................................324.3算法设计...................................................................................334.3.1算法流程图.....................................................................334.3.2染色体结构.....................................................................334.3.3约束处理........................................................................354.3.4适应度函数.....................................................................364.3.5初始种群........................................................................364.3.6遗传算子........................................................................364.3.7控制参数和终止条件.......................................................374.4算法实现...................................................................................394.5实验及结果分析........................................................................394.5.1控制参数选定.................................................................394.5.2实例实验........................................................................434.5.3实例数据........................................................................444.5.4实例数据分析.................................................................44结论.........................................................................................................45参考文献...................................................................................................47谢辞.........................................................................................................48

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