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

doi:10.3969/j.issn.1007-7375.2020.05.012

Abstract

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

CLC Number:

WANG Sulin, QIU Feier, CHEN Fan, LIU Chuankun, LU Teng, WANG Zhiyun. A Research on Underground Logistics Node Selection Planning Based on Greedy Genetic Algorithm[J]. Industrial Engineering Journal, 2020, 23(5): 88-95.

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