Optimal maintenance strategy for large-scale production systems under maintenance time uncertainty

Haibo Jin, Xianhe Song and Hao Xia

Reliability Engineering and System Safety, 2023, vol. 240, issue C

Abstract: Many complex industrial systems have numerous operational states and oftentimes suffer from a variety of uncertainties. Determining how to address the uncertainty and efficiently reduce the number of system states has practical significance. This work is dedicated to developing a maintenance strategy based on the approximate dynamic programming (ADP) method for large-scale maintainable systems suffering from maintenance time uncertainties. In this work, an optimal schedule algorithm is proposed to address the optimal assignment problem of how to assign a certain number of components to several technicians, with the goal of minimizing the total maintenance time subject to maintenance time uncertainty. Thereafter, an optimal maintenance strategy for a system with a large number of states over a finite time horizon is developed based on the Markov decision process combined with ADP. To solve such a maintenance strategy, a solution algorithm is proposed where the system state reached in the next decision period is estimated by a simulation technique, and the post-decision state method is utilized to achieve the reduction of the number of system ergodic states. The results of numerical examples and a practical application demonstrate the effectiveness, high performance and applicability of the developed strategy.

Keywords: Optimal maintenance strategy; Multi-state system; Post-decision state; Uncertainty (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:240:y:2023:i:c:s0951832023005082

DOI: 10.1016/j.ress.2023.109594

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