Two-stage adaptive population whale optimization algorithm for solving double flexible job shop scheduling problems

Bingrui Zhu; Yuchuan Song; Qi Lei

doi:10.1117/12.2688697

18 July 2023 Two-stage adaptive population whale optimization algorithm for solving double flexible job shop scheduling problems

Bingrui Zhu, Yuchuan Song, Qi Lei

Author Affiliations +

Proceedings Volume 12744, Second International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2023); 127440Z (2023) https://doi.org/10.1117/12.2688697
Event: Second International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2023), 2023, Nanjing, China

Abstract

This paper considers the double flexible job shop scheduling problem of both workers and machines with a background of high-efficiency and energy-saving flexible job shop scheduling. Based on this problem, a multi-objective optimization mathematical model of the time index, green production index and worker factor index is established. Aiming at this mathematical model, an improved whale optimization algorithm (MAWOA) is proposed. A two-stage population adaptive strategy is designed, and the calculation of "population life" and the application of neighborhood search are improved. This approach effectively improves the algorithm’s convergence ability and local search capability to achieve a rapid and accurate convergence and ensure the quality of the solution set. Ten kinds of DFJSP benchmark examples are tested, the experimental results show that compared with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and New Hybrid Genetic Algorithm (NHGA), the MAWOA can obtain better quality solutions, which further shows that the proposed algorithm is effective and superior in solving such problems.

Citation Download Citation

Bingrui Zhu, Yuchuan Song, and Qi Lei "Two-stage adaptive population whale optimization algorithm for solving double flexible job shop scheduling problems", Proc. SPIE 12744, Second International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2023), 127440Z (18 July 2023); https://doi.org/10.1117/12.2688697

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