An improved genetic algorithm approach to flexible job shop scheduling

Yibin Lv; Yixin Zhao; Xing Liu; Liexu Xu; Jinlong Zheng; Wencong She

doi:10.1117/12.3039525

28 August 2024 An improved genetic algorithm approach to flexible job shop scheduling

Yibin Lv, Yixin Zhao, Xing Liu, Liexu Xu, Jinlong Zheng, Wencong She

Proceedings Volume 13251, Ninth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024); 1325115 (2024) https://doi.org/10.1117/12.3039525
Event: 9th International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024), 2024, Guilin, China

Abstract

This paper addresses the Flexible Job Shop Scheduling Problem (FJSP) by developing a mathematical model with the goal of minimizing the maximum makespan, and presenting a solution approach using an improved genetic algorithm. The proposed algorithm incorporates optimized encoding and decoding techniques, applies multiple combinatorial crossover operations for process coding and machine coding, and integrates a taboo search algorithm to enhance local search effectiveness. The method is validated using a set of benchmark algorithms for testing. The experimental results indicate that this enhanced genetic algorithm shows considerable improvement in solving FJSP.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yibin Lv, Yixin Zhao, Xing Liu, Liexu Xu, Jinlong Zheng, and Wencong She "An improved genetic algorithm approach to flexible job shop scheduling", Proc. SPIE 13251, Ninth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024), 1325115 (28 August 2024); https://doi.org/10.1117/12.3039525

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