Joint maintenance and spare part ordering from multiple suppliers for multicomponent systems using a deep reinforcement learning algorithm

Meimei Zheng, Zhiyun Su, Dong Wang and Ershun Pan

Reliability Engineering and System Safety, 2024, vol. 241, issue C

Abstract: This paper investigates the joint optimization of maintenance and spare part ordering from multiple suppliers for systems consisting of multiple components. When components degrade to poor conditions, they are replaced with spare parts if available. Spare parts can be purchased from multiple suppliers, each with distinct lead times, unit prices, and setup costs. To determine the optimal replacement and ordering decisions, this paper establishes a model through a Markov decision process and designs a value iteration algorithm to solve the model. However, the value iteration algorithm takes too much time to solve problems involving large numbers of components and suppliers. Thus, we design a hybrid deep reinforcement learning algorithm (HDRL) based on the reinforcement learning algorithm to solve large-scale problems. Numerical experiments are conducted to validate the effectiveness of the HDRL algorithm and analyze the joint decisions. The results show that, compared with the value iteration algorithm, the average cost gap is 3.86%, and the solving time can be reduced by at least 95.99% for systems with more than 2 suppliers and more than 3 components under the HDRL algorithm.

Keywords: Joint optimization; Maintenance; Multiple suppliers; Reinforcement learning; Value iteration (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:241:y:2024:i:c:s0951832023005422

DOI: 10.1016/j.ress.2023.109628

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