多目标下航天产品生产车间柔性资源配置与调度集成优化

To cater to the multi-variety and small-batch characteristics of aerospace product manufacturing, flexible allocation and scheduling of resources in production workshops based on producing planning is key to maintaining efficiency and response capability. In particular, this paper formulates and solves an integrated optimization problem of resource allocation and scheduling to minimize production delay, manufacturing costs, and total resources across multiple scenarios with various production resources, in-workshop transportation, and resource availability. A multi-objective programming model is first formulated based on a detailed delineation of our research questions. We then put forward an improved multi-objective differential evolution (IMODE) algorithm based on Pareto optimization to solve such an NP-hard problem efficiently. Upon obtaining a high-quality set of initial chromosomes based on heuristic search, the final solution of a Pareto set is derived from fast non-dominated sorting followed by iterative optimization through a differential evolution algorithm. Based on 11 scenarios with different combinations of job numbers and due dates generated from empirical data, IMODE on average outperforms NSGA-II and MOPSO, two widely-adopted algorithms for multi-objective optimization. Furthermore, our additional analyses identify a ceiling effect of resource quantity on overdue days and manufacturing costs. When there is a large number of jobs with insufficient resources, on the one hand, increasing the number of resources can mitigate production delays and costs. On the other hand, the allocation of more resources is unable to shorten overdue days or bring down costs in the context of a small job number with adequate production resources. In sum, flexible integrated resource allocation and scheduling through IMODE enhance resource utilization and manufacturing efficiency in the face of dynamic production demands.