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Online Maintenance Prioritization via Monte Carlo Tree Search and Case-based Reasoning

Published

Author(s)

Michael Hoffman, Eunhye Song, Michael Brundage, Soundar Kumara

Abstract

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.
Citation
ASME Journal of Computing and Information Science in Engineering
Volume
22
Issue
4

Keywords

maintenance, markov decision process, monte carlo tree search, case-based reasoning

Citation

Hoffman, M. , Song, E. , Brundage, M. and Kumara, S. (2022), Online Maintenance Prioritization via Monte Carlo Tree Search and Case-based Reasoning, ASME Journal of Computing and Information Science in Engineering, [online], https://doi.org/10.1115/1.4053408, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932724 (Accessed May 23, 2024)

Issues

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Created August 7, 2022, Updated November 29, 2022