Multi-objective storage location allocation optimization and simulation analysis of automated warehouse based on multi-population genetic algorithm. (December 2018)

• Record Type: Journal Article
• Authors: Jiao, Yu-ling; Xing, Xiao-cui; Zhang, Peng; Xu, Liang-cheng; Liu, Xin-Ran
• Is Part Of: Concurrent engineering, research and applications. Volume 26:Number 4(2018:Dec.)
• Page Start: 367
• Page End: 377
• Publication Date: 2018-12
• Subjects: storage location allocation -- multi-population genetic algorithm -- multi-objective programming -- automated warehouse -- FlexSim simulation; Production engineering -- Periodicals; Concurrent engineering -- Periodicals; 621.39
• DOI: 10.1177/1063293X18796365
• Languages: English
• ISSNs: 1063-293X

Abstract

Aiming at the requirement of working efficiency and security of automated warehouse and taking the operation time of outbound–inbound, the equivalent center of gravity of overall shelf and the degree of relative accumulation of related products as the multi-objective functions, the mathematical model is constructed for multi-objective storage location allocation optimization. According to the simple weighted genetic algorithm, it is easily prone to the problem of immature convergence when solving multi-objective programming problems. So, the multi-population genetic algorithm is proposed to solve the mathematical model of storage location allocation optimization. Combining with the experiment data of toy car assembly and automated warehouse, the results of the automated warehouse storage location allocation are obtained. FlexSim dynamic simulation model is established based on the storage location allocation solution, the physical parameters of automated warehouse and the experimental requirements plan of vehicle model assembly. The operation effect of the model and the utilization rate of the equipment are analyzed. The result of multi-population genetic algorithm is more reasonable and effective. It is proved that the result of multi-population genetic algorithm is superior to the result of simple weighted genetic algorithm, which provides an effective method for storage location allocation optimization and outbound–inbound dynamic simulation.