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摘要
We consider a parallel machine scheduling problem with the objective of minimizing two types of costs: the cost related to production operations and the cost related to due date performances. The former could be reduced by reasonable settings of the operational variables (e.g., the number of workers, the frequency of maintenance), while the latter could be reduced by appropriate scheduling of the production process. However, the optimization of both targets is significantly complicated by the influence of human factors that play a dominant role in real-world manufacturing systems. To cope with this issue, a simulation-based optimization framework is adopted in this paper for obtaining high-quality robust solutions to the integrated scheduling problem. Meanwhile, differential evolution, a metaheuristic algorithm based on swarm intelligence, is applied for a systematic search of the huge solution space. Finally, numerical computations are conducted to verify the effectiveness of the proposed approach. Sensitivity analysis and practical implications are also presented.
