Research on flexible job-shop scheduling strategy based on automata and genetic algorithm

Chan Gu; Shengli Ye; Junbo Zhao

doi:10.1117/12.2683546

26 June 2023 Research on flexible job-shop scheduling strategy based on automata and genetic algorithm

Chan Gu, Shengli Ye, Junbo Zhao

Proceedings Volume 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023); 127211Q (2023) https://doi.org/10.1117/12.2683546
Event: 2023 2nd International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2023, Chengdu, China

Abstract

Flexible job-shop scheduling problem (FJSP) is an extension of classical job-shop scheduling problem, which is a typical NP difficult problem with complex modeling and solving difficulties. In order to solve the problems of long coding length and low efficiency of solving FJSP by two-layer coding genetic algorithm, a FJSP solving algorithm based on automata and genetic algorithm is proposed in this paper. For machining automaton model is established, and then according to priority machine automata model generation chromosomes, through genetic algorithm for scheduling policy. Simulation results show that the proposed algorithm is faster and more efficient than the traditional two-layer coding genetic algorithm.

Citation Download Citation

Chan Gu, Shengli Ye, and Junbo Zhao "Research on flexible job-shop scheduling strategy based on automata and genetic algorithm", Proc. SPIE 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 127211Q (26 June 2023); https://doi.org/10.1117/12.2683546

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