基于深度强化学习的预防性维护,适用于流水线系统中出现劣化的可维修机器

IF 4.4 3区 管理学 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Annals of Operations Research Pub Date : 2024-08-06 DOI:10.1007/s10479-024-06207-x
Yu-Hsin Hung, Hong-Ying Shen, Chia-Yen Lee
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引用次数: 0

摘要

在制造系统中,预防性维护对保持产品产量、产品质量和机器可靠性起着至关重要的作用。不恰当的维护策略会导致低产量、次品、机器故障以及上下游机器运行中断。然而,在随机工厂环境中制定维护策略具有挑战性,原因包括不同程度的劣化、不可预测的维护时间和波动的机器工作量。鉴于以往的研究使用马尔可夫决策过程来制定维护决策,我们提出了一种深度强化学习方法来推导维护策略。我们还考虑了多目标方法 hypervolume,以说明维护成本、生产损失和产量损失之间的权衡。模拟研究表明,我们提出的方法在十种不同的情况下都优于年龄依赖策略和运行至故障策略。除了获得最佳近似策略外,可视化行动轨迹还为优化和平衡不同成本提供了管理见解。此外,实施由我们提出的方法衍生出的预防性维护策略可以增强供应链运营的稳健性。通过降低意外设备故障的风险,供应链可以实现更高水平的运营可靠性和连续性。
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Deep reinforcement learning-based preventive maintenance for repairable machines with deterioration in a flow line system

In manufacturing systems, preventive maintenance plays a critical role in maintaining product yield, product quality, and machine reliability. Inappropriate maintenance strategies can lead to low yields, faulty products, machine failures, and disrupted operation of upstream and downstream machines. However, developing maintenance strategies in a stochastic factory environment can be challenging due to factors such as varying levels of deterioration, unpredictable maintenance times, and fluctuating machine workloads. Since previous studies formulated the maintenance decision using a Markov decision process, we propose a deep reinforcement learning method to derive the maintenance policy. We also consider the multi-objective method, hypervolume, to illustrate the trade-off between maintenance cost, production loss, and yield loss. The simulation study shows that our proposed method outperforms age-dependent and run-to-failure strategies in ten different scenarios. In addition to obtaining an optimal approximate policy, visualizing action trajectories provides managerial insights for optimizing and balancing different costs. Moreover, implementing preventive maintenance policies derived from our proposed method can enhance the robustness of supply chain operations. By reducing the risk of unexpected equipment failures, supply chains can achieve higher levels of operational reliability and continuity.

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来源期刊
Annals of Operations Research
Annals of Operations Research 管理科学-运筹学与管理科学
CiteScore
7.90
自引率
16.70%
发文量
596
审稿时长
8.4 months
期刊介绍: The Annals of Operations Research publishes peer-reviewed original articles dealing with key aspects of operations research, including theory, practice, and computation. The journal publishes full-length research articles, short notes, expositions and surveys, reports on computational studies, and case studies that present new and innovative practical applications. In addition to regular issues, the journal publishes periodic special volumes that focus on defined fields of operations research, ranging from the highly theoretical to the algorithmic and the applied. These volumes have one or more Guest Editors who are responsible for collecting the papers and overseeing the refereeing process.
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