Reinforcement learning in reliability and maintenance optimization: A tutorial

Qin Zhang, Yu Liu, Yisha Xiang and Tangfan Xiahou

Reliability Engineering and System Safety, 2024, vol. 251, issue C

Abstract: The increasing complexity of engineering systems presents significant challenges in addressing intricate reliability and maintenance optimization problems. Advanced computational techniques have become imperative. Reinforcement learning, with its strong capability of solving complicated sequential decision-making problems under uncertainty, has emerged as a powerful tool to reliability and maintainability community. This paper offers a step-by-step guideline on the utilization of reinforcement learning algorithms for resolving reliability optimization and maintenance optimization problems. We first introduce the Markov decision process modeling framework for these problems and elucidate the design and implementation of solution algorithms, including dynamic programming, reinforcement learning, and deep reinforcement learning. Case studies, including a pipeline system mission abort optimization and a manufacturing system condition-based maintenance decision-making, are included to demonstrate the utility of reinforcement learning in reliability and maintenance applications. This tutorial will assist researchers in the reliability and maintainability community by summarizing the state-of-the-art reinforcement learning algorithms and providing the hand-on implementations in reliability and maintenance optimization problems.

Keywords: Markov decision process; Reinforcement learning; Reliability optimization; Maintenance optimization (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:251:y:2024:i:c:s0951832024004733

DOI: 10.1016/j.ress.2024.110401

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