MGA for studying classified JSP

Tao Ze; Longning Jiang; Yilan Sun; Tiejun Wang

doi:10.1117/12.2674869

2 May 2023 MGA for studying classified JSP

Tao Ze, Longning Jiang, Yilan Sun, Tiejun Wang

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 1264213 (2023) https://doi.org/10.1117/12.2674869
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

For improving the behavior of the traditional genetic algorithm (GA) for solving job shop scheduling problems(JSP), a modified genetic algorithm(MGA) is proposed. The reform is reflected in the generation of the initial population and the decoding methods are different. The initial population is produced randomly and the job with the largest remained total processing time is preferred. In decoding, the machine can be selected according to the shortest processing time of the currently available machine and the earliest completion time of the currently available machine. At the same time, considering that dynamic events are inevitable in the process of job shop scheduling, it is more practically in application than static scheduling. Dynamic events are classified according to the degree of disturbance. The change in completion time and the change of processing machine are selected for some operations. Finally, the modified algorithm and the technique of dynamic classified job shop scheduling are proved to be feasible according to the case analysis.

Citation Download Citation

Tao Ze, Longning Jiang, Yilan Sun, and Tiejun Wang "MGA for studying classified JSP", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 1264213 (2 May 2023); https://doi.org/10.1117/12.2674869

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