Condition-based maintenance for redundant systems considering spare inventory with stochastic lead time
•Stochastic maintenance time and spare ordering lead time are considered.•An improved Q-learning algorithm is proposed with convergence proved.•The effectiveness of the proposed algorithm is shown by numerical examples.•Simulated environment based on discrete event simulation method is established....
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| Published in: | Reliability engineering & system safety Vol. 257; p. 110837 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.05.2025
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| ISSN: | 0951-8320 |
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| Abstract | •Stochastic maintenance time and spare ordering lead time are considered.•An improved Q-learning algorithm is proposed with convergence proved.•The effectiveness of the proposed algorithm is shown by numerical examples.•Simulated environment based on discrete event simulation method is established.
Condition-based maintenance (CBM) and spare provisioning are both important to guarantee the operation of redundant systems composed of degrading components. However, most existing studies on joint optimization of CBM and spare inventory assume maintenance actions are instantaneous and the lead time for spares are fixed, which are not consistent with the reality. Therefore, this paper focus on the joint optimization problems to minimize the total cost rate considering stochastic maintenance time for components and stochastic lead time for spares. The problem is modeled as a Markov decision process model and solved by an improved reinforcement learning algorithm, i.e., the improved Q-learning algorithm, which converges more quickly and reaches a smaller value of the total cost rate than the traditional Q-learning algorithm. Moreover, the simulated environment based on discrete event simulation method is introduced in detail and the convergence of the algorithm is proved theoretically. Based on the numerical study, we further demonstrate the convergence and effectiveness of the proposed algorithm and perform sensitivity analysis on several model parameters to provide management insights for decision makers. |
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| AbstractList | •Stochastic maintenance time and spare ordering lead time are considered.•An improved Q-learning algorithm is proposed with convergence proved.•The effectiveness of the proposed algorithm is shown by numerical examples.•Simulated environment based on discrete event simulation method is established.
Condition-based maintenance (CBM) and spare provisioning are both important to guarantee the operation of redundant systems composed of degrading components. However, most existing studies on joint optimization of CBM and spare inventory assume maintenance actions are instantaneous and the lead time for spares are fixed, which are not consistent with the reality. Therefore, this paper focus on the joint optimization problems to minimize the total cost rate considering stochastic maintenance time for components and stochastic lead time for spares. The problem is modeled as a Markov decision process model and solved by an improved reinforcement learning algorithm, i.e., the improved Q-learning algorithm, which converges more quickly and reaches a smaller value of the total cost rate than the traditional Q-learning algorithm. Moreover, the simulated environment based on discrete event simulation method is introduced in detail and the convergence of the algorithm is proved theoretically. Based on the numerical study, we further demonstrate the convergence and effectiveness of the proposed algorithm and perform sensitivity analysis on several model parameters to provide management insights for decision makers. |
| ArticleNumber | 110837 |
| Author | Zhou, Jian Yang, Yating Yang, Lechang Wang, Jun Fu, Yuqiang |
| Author_xml | – sequence: 1 givenname: Jun orcidid: 0000-0001-8261-1371 surname: Wang fullname: Wang, Jun organization: International Business School, Beijing Foreign Studies University, Beijing, China – sequence: 2 givenname: Yuqiang orcidid: 0000-0003-0864-1243 surname: Fu fullname: Fu, Yuqiang email: fuyuqiang@ustb.edu.cn organization: School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China – sequence: 3 givenname: Jian orcidid: 0000-0003-3505-8117 surname: Zhou fullname: Zhou, Jian organization: School of Economics and Management, Nanjing University of Science and Technology, Nanjing, Jiangsu, China – sequence: 4 givenname: Lechang orcidid: 0000-0003-3895-0717 surname: Yang fullname: Yang, Lechang organization: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China – sequence: 5 givenname: Yating surname: Yang fullname: Yang, Yating organization: International Business School, Beijing Foreign Studies University, Beijing, China |
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| Keywords | Improved reinforcement learning Stochastic lead time Condition-based maintenance Spare inventory |
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| SubjectTerms | Condition-based maintenance Improved reinforcement learning Spare inventory Stochastic lead time |
| Title | Condition-based maintenance for redundant systems considering spare inventory with stochastic lead time |
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