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- W4384697802 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." @default.
- W4384697802 created "2023-07-20" @default.
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- W4384697802 date "2023-07-18" @default.
- W4384697802 modified "2023-09-23" @default.
- W4384697802 title "Two-stage adaptive population whale optimization algorithm for solving double flexible job shop scheduling problems" @default.
- W4384697802 doi "https://doi.org/10.1117/12.2688697" @default.
- W4384697802 hasPublicationYear "2023" @default.
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