The distributed flexible job shop scheduling problem(DFJSP)garnered significant attention in line with the expansion of the global manufacturing industry.However,the previous DFJSP research ignored worker constraints.As one critical factor of production,the effective utilization of worker resources increased productivity.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption was studied in this paper.To solve the problem,a multi-objective mathematical model for DFJSP-DRC and an improved non-dominated sorting genetic algorithm(INSGA-II)were proposed.In INSGA-II,high-quality initial solutions were generated using a hybrid initialization strategy,and an active decoding strategy based on the public idle time of processing machines and workers was designed to derive the scheduling scheme.To enhance the global search capability of INSGA-II,an improved cross-mutation strategy and an adaptive cross-mutation rate were proposed.The effectiveness of INSGA-II in addressing DFJSP-DRC was verified through 45 comprehensive experiment instances compared with three algorithms.
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