Multi AGV Scheduling Optimization of AutoStore System Based on Improved Multi Population Genetic Algorithm

doi:10.3969/j.issn.1007-7375.2021.04.013

Abstract

Abstract: The emerging compact-intensive storage system AutoStore has the coexistence of separate operations and joint operations for outbound and inbound operations. If the AGV scheduling scheme obtained under the traditional single operation mode is used, it is easy to cause resource waste or low efficiency. Therefore, based on the analysis of multi-operation mode process, the AGV task allocation model with the shortest total operation time of various operation modes is established, and the objective function is the shortest total operation time of the system. The traditional multi-population genetic algorithm is improved. Firstly, in order to make the distribution of the initial solution uniform, the distribution of the generated initial solution is judged. Secondly, the rule that the cross-mutation probability changes with the fitness value is given to enhance the search efficiency of the algorithm. The analysis of the example verifies the feasibility and effectiveness of the improved algorithm, which can provide a better system for the system AGV scheduling scheme.

Key words: AutoStore system, AGV scheduling, multi-population genetic algorithm, joint job

CLC Number:

TP399

WANG Xiaojun, WANG Bo, JIN Minjie, YANG Chunxia, BAI Xinli. Multi AGV Scheduling Optimization of AutoStore System Based on Improved Multi Population Genetic Algorithm[J]. Industrial Engineering Journal, 2021, 24(4): 112-118,167.

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