Optimal data pooling for shared learning in maintenance operations

IF 0.8 4区 管理学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Operations Research Letters Pub Date : 2024-01-01 DOI:10.1016/j.orl.2023.11.009
Collin Drent, Melvin Drent, Geert-Jan van Houtum
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Abstract

We study optimal data pooling for shared learning in two common maintenance operations: condition-based maintenance and spare parts management. We consider systems subject to Poisson input – the degradation or demand process – that are coupled through an unknown rate. Decision problems for these systems are high-dimensional Markov decision processes (MDPs) and are thus notoriously difficult to solve. We present a decomposition result that reduces such an MDP to two-dimensional MDPs, enabling structural analyses and computations. Leveraging this decomposition, we (i) show that pooling data can lead to significant cost reductions compared to not pooling, and (ii) prove that the optimal policy for the condition-based maintenance problem is a control limit policy, while for the spare parts management problem, it is an order-up-to level policy, both dependent on the pooled data.

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维护操作中共享学习的最佳数据池
我们研究了基于状态的维护和备件管理这两种常见维护操作中共享学习的最佳数据池。我们考虑的是受泊松输入(退化或需求过程)影响的系统,这些系统通过未知的速率耦合在一起。这些系统的决策问题是高维马尔可夫决策过程(MDP),因此众所周知难以解决。我们提出了一种分解结果,可将此类 MDP 简化为二维 MDP,从而实现结构分析和计算。利用这一分解结果,我们(i) 证明了与不汇集数据相比,汇集数据可显著降低成本;(ii) 证明了基于状态的维护问题的最优策略是控制极限策略,而备件管理问题的最优策略是订单到水平策略,两者都取决于汇集的数据。
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来源期刊
Operations Research Letters
Operations Research Letters 管理科学-运筹学与管理科学
CiteScore
2.10
自引率
9.10%
发文量
111
审稿时长
83 days
期刊介绍: Operations Research Letters is committed to the rapid review and fast publication of short articles on all aspects of operations research and analytics. Apart from a limitation to eight journal pages, quality, originality, relevance and clarity are the only criteria for selecting the papers to be published. ORL covers the broad field of optimization, stochastic models and game theory. Specific areas of interest include networks, routing, location, queueing, scheduling, inventory, reliability, and financial engineering. We wish to explore interfaces with other fields such as life sciences and health care, artificial intelligence and machine learning, energy distribution, and computational social sciences and humanities. Our traditional strength is in methodology, including theory, modelling, algorithms and computational studies. We also welcome novel applications and concise literature reviews.
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