Online Maintenance Prioritization Via Monte Carlo Tree Search and Case-Based Reasoning
Online Maintenance Prioritization Via Monte Carlo Tree Search and Case-Based Reasoning
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DOI：
10.1115/1.4053408
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发表时间：
2022-01
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影响因子：
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通讯作者：
Michael Hoffman;Eunhye Song;Michael Brundage;S. Kumara
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作者：
Michael Hoffman;Eunhye Song;Michael Brundage;S. Kumara
当制造系统中的维护资源是有限的，在确定如何分配这些资源之间的多个竞争的维护工作的挑战。我们制定这个问题作为一个在线的优先级问题，使用马尔可夫决策过程（MDP）模型的系统行为和蒙特卡洛树搜索（MCTS），以寻求最佳的维护行动，在系统的各种状态。此外，我们使用基于案例的推理（CBR）保留和重用从MCTS收集的搜索经验，以减少随着时间的推移所需的计算工作量，并提高决策效率。我们证明，我们提出的方法相比，现有的方法的维护优先级，同时也减少了所需的时间来确定最佳的维护行动，收集更多的经验时，系统的吞吐量增加。这在必须快速做出维护决策的制造环境中尤其有益。
When maintenance resources in a manufacturing system are limited, a challenge arises in determining how to allocate these resources among multiple competing maintenance jobs. We formulate this problem as an online prioritization problem using a Markov decision process (MDP) to model the system behavior and Monte Carlo tree search (MCTS) to seek optimal maintenance actions in various states of the system. Further, we use Case-based Reasoning (CBR) to retain and reuse search experience gathered from MCTS to reduce the computational effort needed over time and to improve decision-making efficiency. We demonstrate that our proposed method results in increased system throughput when compared to existing methods of maintenance prioritization while also reducing the time needed to identify optimal maintenance actions as more experience is gathered. This is especially beneficial in manufacturing settings where maintenance decisions must be made quickly.