学术分享|贾彦鹤-Q-learning driven multi-population memetic algorithm for distributed three-stage assembly hybrid flow shop scheduling with flexible preventive maintenance
【文章摘要】The distributed assembly flow shop scheduling (DAFS) problem has received much attention in the last decade, and a variety of metaheuristic algorithms have been developed to achieve the high-quality solution. However, there are still some limitations. On the one hand, these studies usually ignore the machine deterioration, maintenance, transportation as well as the flexibility of flow shops. On the other hand, metaheuristic algorithms are prone to fall into local optimality and are unstable in solving complex combinatorial optimization problems. Therefore, a multi-population memetic algorithm (MPMA) with Q-learning (MPMA-QL) is developed to address a distributed assembly hybrid flow shop scheduling problem with flexible preventive maintenance (DAHFSP-FPM). Specifically, a mixed integer linear programming (MILP) model targeted at the minimal makespan is first established, followed by an effective flexible maintenance strategy to simplify the model. To efficiently solve the model, MPMA is developed and Q-learning is used to achieve an adaptive individual assignment for each subpopulation to improve the performance of MPMA. Finally, two state-of-the-art metaheuristics and their Q-learning-based improvements are selected as rivals of the developed MPMA and MPMA-QL. A series of numerical studies are carried out along with a real-life case of a furniture manufacturing company, to demonstrate that MPMA-QL can provide better solutions on the studied DAHFSP-FPM..
【关键词】Distributed hybrid flow shop；Transportation and assembly；Preventive maintenance；Meta-heuristics；Reinforcement learning；Integration
【文章作者】贾彦鹤
【作者单位】伟德bv国际体育
【发表期刊】Expert Systems With Applications
【发表时间】2023年6月
【基金资助】This work was supported in part by the National Key Research and Development Program of China under Grant no. 2021YFF0901300, in part by the National Natural Science Foundation of China under Grant nos. 62173076, 72271048 and in part by the China Scholarship Council under Grant no. 202206080076.
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