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多需求多类型自提点选址分配问题 被引量:10

Location and assignment problem of pickup points under multiple types of demands
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摘要 为了降低快递企业成本、提高市场竞争力,研究了多需求多类型自提点的选址分配问题,以顾客自提和退换货两种需求为例,在考虑3种不同类型自提点的建设成本、服务能力、顾客满意度等的前提下,建立了以快递企业总成本最小化为目标的混合整数规划模型。根据模型可以确定各类型自提点的建立位置,以及各个自提点服务的需求点集合,进一步根据多需求多类型自提点选址分配问题的特征设计了求解模型的遗传算法。利用具体算例进行了模拟计算,结果显示,所设计的遗传算法能有效求解带容量约束和顾客满意度约束的多需求多类型自提点选址分配问题,研究成果为解决快递企业末端配送设施选址分配问题提供了理论依据。 To reduce the cost of express enterprise and improve the market competitiveness,the location and assignment problem of pickup points under multiple types of demands is investigated.The location and assignment problem of pickup points under multiple types of demands was investigated.By taking two types of customer demand including self-service and return-exchange as an example,the construction cost,service capability and customer satisfaction of three different types of pickup points were considered to formulate a mixed integer programming model for minimizing the total cost of pickup points.According to the model,the location of each selected pickup point could be determined,and the demand points served by each established pickup point were determined.Furthermore,the genetic algorithm was designed to solve the model based on the problems feature.The simulation results showed that the proposed genetic algorithm could effectively solve the location and assignment problem of pickup points under multiple types of demands with capacity constraints and customer satisfaction constraints.The research could provide a theoretical basis for solving the location and assignment problem of terminal distribution facilities in express enterprises.
作者 李珍萍 毛小寸 LI Zhenping;MAO Xiaocun(School of Information,Beijing Wuzi University,Beijing 101149,China)
出处 《计算机集成制造系统》 EI CSCD 北大核心 2018年第11期2889-2897,共9页 Computer Integrated Manufacturing Systems
基金 国家自然科学基金资助项目(71771028 71540028) 北京市高水平创新团队资助项目(IDHT20180510) 北京市智能物流协同创新中心资助项目~~
关键词 多需求 多类型自提点 选址分配问题 整数规划模型 遗传算法 multiple types of demands multiple types of pickup points location and assignment problem integer programming model genetic algorithms
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