Journal of Jilin University(Engineering and Technology Edition) ›› 2018, Vol. 48 ›› Issue (5): 1398-1404.doi: 10.13229/j.cnki.jdxbgxb20170691

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Slotting optimization of automated warehouse based on multi-population GA

JIAO Yu-ling, ZHANG Peng, TIAN Guang-dong, XING Xiao-cui, ZOU Lian-hui   

  1. College of Transportation, Jilin University, Changchun 130022,China
  • Received:2017-07-01 Online:2018-09-20 Published:2018-12-11

Abstract: To meet the requirement of the working performance and security of automated warehouse, the mathematical model of multi-objective slotting allocation optimization is constructed. The outbound-inbound operation time, the equivalent center of gravity of overall shelf and the degree of relative accumulation of related products are taken as the multi-objective functions. A Simple Weighted Genetic Algorithm (SWGA) is used to solve the model obtained by weighted and normalized multi-objective models. To avoid the problem of premature convergence, a Multi-population Genetic Algorithm (MPGA) is proposed to solve the optimization model of slotting allocation. Combined with experiment of toy car assembly and experiment of automated warehouse, the results of slotting allocation are obtained. The effectiveness of the MPGA is verified by comparison and analysis of the two results. This study provides an effective solution for automated warehouse slotting allocation and optimization.

Key words: industriol design, warehouse slotting allocation, multi-population genetic algorithm, multi-objective planning

CLC Number: 

  • TB491
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