基于时空聚类求解带容积约束的选址-路径问题
作者:
作者单位:

重庆交通大学

作者简介:

通讯作者:

中图分类号:

F224

基金项目:

教育部人文社科一般项目;中国博士后面上项目;重庆社科


Study on Capacitate Location-routing Problem Based on Time-space Cluster
Author:
Affiliation:

Chongqing Jiaotong University

Fund Project:

General Program of Humanities and Social Sciences of the Ministry of Education(19YJC630198);China Postdoctoral Program(2019M653345);Chongqing Social Sciences,China(2017YBGL154)

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    摘要:

    选址-路径问题是供应链管理和物流系统规划中一个重要问题,对总成本具有十分重要的影响。本文对考虑配送中心容积约束的带时间窗的选址-路径问题进行了研究,建立了以总成本最小和客户满意度最大为目标的多目标规划模型,提出了两阶段算法对其进行求解。首先利用k-means聚类算法确定配送中心选址,而后提出了一种基于时间-空间双因素的客户划分方法以确定配送中心所服务客户,最后,利用粒子群算法对各配送中心的配送路径进行规划。两个数值算例表明,本文所提出的算法较其它已有算法,均能有效的降低物流运作总成本及总配送路径长度,为解决带容积约束及时间窗的的选址-路径问题提供一种新的解决思路。

    Abstract:

    Location-Routing is an important problem in supply chain management and logistic systems. This paper studies the Location-Routing problem with time windows with the consideration of the distribution centers capacitate constraints. It establishes a multi-objective programming model to minimize the total cost and maximize the customer satisfaction, then proposes a two-stage algorithm to solve the proposed model. Firstly, k-means clustering algorithm was used to determine the location of distribution centers, and then a time-space two-factor customer division method was proposed to determine the customers served by the chosen distribution center. Finally, particle swarm algorithm was used to optimize the vehicle routings of each distribution center.Two numerical examples show that the proposed algorithm can effectively reduce the total cost of logistics operation and the total distribution routing length compared with other existing algorithms, providing a new solution to the location-routing problem with capacitate constraint and time windows.

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历史
  • 收稿日期:2020-01-16
  • 最后修改日期:2020-06-26
  • 录用日期:2020-07-01
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