doi:10.16186/j.cnki.1673-9787.2020070062

Received:2020/07/17

Revised:2020/10/22

Published:2022/03/15

Research on improved genetic algorithm for job-shop scheduling problem based on association rules

QIAO Dongping 1,2, BAI Wentong 1,2, WEN Xiaoyu 1,2, LI Hao 1,2, Wang Yajing 1,2

1.College of Mechanical and Electrical Engineering, Zhengzhou University of  Light Industry, Zhengzhou  450002 , Henan, China;2.Henan Key Laboratory of  Intelligent Manufacturing of Mechanical Equipment, Zhengzhou  450002 , Henan, China

Abstract: Aim to the problem that initial population had a greater impact on the results of genetic algorithm solving job shop scheduling,an association rules improvement genetic algorithm( AR-GA) was proposed to improve the performance of the algorithm.Association rules were introduced to obtain frequent process blocks of the gene sequence in population initialization stage.According to the number of crossover individuals with frequent process blocks,three different crossover methods were designed in the crossover stage.The Segmented Hamming distance was adopted to guide mutation of the offspring population in the mutation stage,and the information of frequent process blocks was updated after each-iteration.Standard case test results showed that the improved algorithm had higher efficiency and better stability when addressing job shop scheduling problem.

Key words:job-shop scheduling;initial population;genetic algorithm;association rule

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