增强型多目标进化算法与强化学习用于灵活作业车间的节能调度

IF 2.8 4区 工程技术 Q2 ENGINEERING, CHEMICAL Processes Pub Date : 2024-09-13 DOI:10.3390/pr12091976
Jinfa Shi, Wei Liu, Jie Yang
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引用次数: 0

摘要

灵活作业车间调度问题(FJSP)的研究对绿色制造具有重要意义。本文以最大完成时间最小化和机器总能耗最小化为优化目标,提出了一种基于强化学习的改进型多目标分解进化算法(MOEA/D)。首先,采用三种初始化策略按一定比例生成初始种群,并结合四种可变邻域搜索策略提高算法的局部搜索能力。其次,提出了一种基于 Q-learning 的参数适应策略,引导种群选择最优参数以提高多样性。最后,通过不同规模的 Kacem 和 BRdata 基准案例以及汽车发动机冷却系统制造的生产实例,将 Q-MOEA/D 与 IMOEA/D 和 NSGA-II 进行比较,分析和评估了所提算法的性能。结果表明,Q-MOEA/D 算法在解决灵活作业车间的节能调度问题方面优于其他两种算法。
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An Enhanced Multi-Objective Evolutionary Algorithm with Reinforcement Learning for Energy-Efficient Scheduling in the Flexible Job Shop
The study of the flexible job shop scheduling problem (FJSP) is of great importance in the context of green manufacturing. In this paper, with the optimization objectives of minimizing the maximum completion time and the total machine energy consumption, an improved multi-objective evolutionary algorithm with decomposition (MOEA/D) based on reinforcement learning is proposed. Firstly, three initialization strategies are used to generate the initial population in a certain ratio, and four variable neighborhood search strategies are combined to increase the local search capability of the algorithm. Second, a parameter adaptation strategy based on Q-learning is proposed to guide the population to select the optimal parameters to increase diversity. Finally, the performance of the proposed algorithm is analyzed and evaluated by comparing Q-MOEA/D with IMOEA/D and NSGA-II through different sizes of Kacem and BRdata benchmark cases and production examples of automotive engine cooling system manufacturing. The results show that the Q-MOEA/D algorithm outperforms the other two algorithms in solving the energy-efficient scheduling problem for flexible job shops.
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来源期刊
Processes
Processes Chemical Engineering-Bioengineering
CiteScore
5.10
自引率
11.40%
发文量
2239
审稿时长
14.11 days
期刊介绍: Processes (ISSN 2227-9717) provides an advanced forum for process related research in chemistry, biology and allied engineering fields. The journal publishes regular research papers, communications, letters, short notes and reviews. Our aim is to encourage researchers to publish their experimental, theoretical and computational results in as much detail as necessary. There is no restriction on paper length or number of figures and tables.
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