工业工程 ›› 2020, Vol. 23 ›› Issue (5): 88-95.doi: 10.3969/j.issn.1007-7375.2020.05.012

• 专题论述 • 上一篇    下一篇

基于贪心遗传的地下物流节点选择规划研究

王苏林1, 邱菲尔2, 陈凡3, 刘川昆3, 鹿腾4, 王芷芸4   

  1. 1. 西南交通大学希望学院 轨道交通学院,四川 南充 610400;
    2. 西南交通大学 交通运输与物流学院, 四川 成都 611756;
    3. 西南交通大学 土木工程学院, 四川 成都 610031;
    4. 西南交通大学 信息科学与技术学院,四川 成都 611756
  • 收稿日期:2019-05-28 发布日期:2020-10-30
  • 通讯作者: 刘川昆(1996-),男,重庆市人,硕士研究生,主要研究方向为地下空间开发及管理研究.E-mail:liuchuankun96@163.com E-mail:liuchuankun96@163.com
  • 作者简介:王苏林(1984-),女,四川省人,讲师,硕士,主要研究方向为交通运输规划与管理
  • 基金资助:
    国家级大学生创新创业训练计划资助项目(2018106113041);研究生学术素养提升计划资助项目(2019KCJS45)

A Research on Underground Logistics Node Selection Planning Based on Greedy Genetic Algorithm

WANG Sulin1, QIU Feier2, CHEN Fan3, LIU Chuankun3, LU Teng4, WANG Zhiyun4   

  1. 1. School of Rail Transportation, Southwest Jiaotong University Hope College, Nanchong 610400, China;
    2. College of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China;
    3. Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China;
    4. College of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
  • Received:2019-05-28 Published:2020-10-30

摘要: 以国内某地区物流概况为研究背景,结合国内外地下物流系统研究成果,将集合覆盖的思想引入地下物流网络节点选址规划,建立以物流节点数量最少及物流节点转运率最低为优化目标的双层多目标规划模型,并结合贪心算法和遗传算法进行优化求解。研究表明:通过将集合覆盖的思想对城市地下物流系统节点规划进行初步探讨是可行有效的;基于贪心遗传算法进行优化求解,使得该地区地下物流网络节点选择达到全局最优,有效控制了物流节点的数量及节点转运率的大小;地下物流网络节点表现出明显的区域集中性,即服务节点均集中在物流需求点附近,且二级节点服务区域总是邻近某个一级节点。

关键词: 地下物流, 集合覆盖, 双层多目标规划, 贪心遗传算法, 区域集中

Abstract: Taking a local logistics survey as an engineering background, combining with the research results of underground logistics system at home and abroad, the idea of set covering is introduced into the location planning of underground logistics network, a bilevel multiobjective programming model with minimum number of logistics nodes and minimum transfer rate of logistics nodes is established, and combining greedy algorithm and genetic algorithm to solve the optimization problem. The research shows that: It is feasible and effective to introduce the idea of set covering into the planning of underground logistics nodes. Greedy genetic algorithm is used to optimize the solution, which makes the choice of the nodes of the underground logistics network reach the global optimum. The number of nodes and the transfer rate of nodes are effectively controlled. The nodes of the underground logistics network show obvious regional concentration, that is, the service nodes are all concentrated near the logistics demand point, and the two level node service area is always adjacent to a level one node.

Key words: underground logistics, set covering, bilevel multiobjective programming, greedy genetic algorithm, regional concentration

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