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快递公司协同车辆路径问题的优化

ISSN号：1673-2782
期刊名称：《福建电脑》
时间：0
分类：TP301[自动化与计算机技术—计算机系统结构;自动化与计算机技术—计算机科学与技术]
作者机构：[1]广东工业大学自动化学院,广州510006
相关基金：国家自然科学基金（61074147,61074185）; 广东省自然科学基金（S2011010005059,8351009001000002）; 广东省教育部产学研结合项目（2012B091000171,2011B090400460）; 广东省科技计划项目（2012B050600028,2010B090301042）

关键词：硬时间窗, 软时间窗, 外包, 服饰运输调度问题, 混沌人工鱼群算法, 遗传算法, hard time windows, Soft Time Windows, outsourcing, apparel products Vehicle Routing Problem, chaotic artificial fish swarm algorithm, genetic algorithm

中文摘要：

由于服饰产品是一种时效性很强的商品,而且服饰产品在配送过程中可以外包给快递公司进行配送,对带外包和硬时间窗的服饰运输调度问题（Apparel products Vehicle Routing Problem with Hard Time Windows and Outsourcing,AVRPHTWO）进行分析,并构建了AVRPHTWO、一般性VRP（Vehicle Routing Problem）和VRPSTW（Vehicle Routing Problem with Soft Time Windows）的数学模型,通过对基本的人工鱼群算法（artificial fish swarm algorithm,AFSA）进行改进,混沌搜索被引入人工鱼群算法来提高算法的全局收敛性,反馈策略用来指导人工鱼的移动,以此来提高收敛精度。应用混沌人工鱼群算法（chaotic artificial fish swarm algorithm,CAFSA）及遗传算法（genetic algorithm,GA）对所建立的三种模型求解,通过对实验数据进行处理,证明了AVRPHTWO模型和混沌人工鱼群算法求解此类模型的有效性,进一步证明了问题模型的复杂程度影响算法寻优能力,问题模型简单时,遗传算法更优;问题模型复杂时,混沌人工鱼群算法更优。

英文摘要：

Apparel product,as a strong timeliness goods,can be outsourced by the Express Company in the process of delivery.This paper analyzes the Apparel products Vehicle Routing Problem with Hard Time Windows and Outsourcing（ AVRPHTWO）,and then builds the AVRPHTWO model,Vehicle Routing Problem with Soft Time Windows（ VRPSTW） model and general mathematical model of Vehicle Routing Problem（ VRP）,improving the basic artificial fish swarm algorithm（ AFSA）,introducing the chaotic search in order to improve the global convergence of the artificial fish swarm algorithm,using feedback strategy to guide the movement of the artificial fish,in order to improve the convergence precision. By way of chaotic artificial fish swarm algorithm（ CAFSA）and genetic algorithm（ GA） to solve the three kinds of model,based on the experimental data processing,it can prove the validity of AVRPHTWO model and the chaotic artificial fish swarm algorithm to solve the kinds of model; moreover,it further proves that the complexity of the model can affect the optimization ability; the simpler the problem model is,the better genetic algorithm is; the more complex the problem model is,the better chaotic artificial fish swarm algorithm is.

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