A state-specific joint size, maintenance, and inventory policy for a k-out-of-n load-sharing system subject to self-announcing failures

Making optimal decisions for system size and its components’ replacement and spare parts’ replenishment is crucial for the normal operation of a k-out-of-n system whose components deteriorate over time. This task is challenging when the components have load-dependent deterioration rates and the syst...

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Published inReliability engineering & system safety Vol. 257; p. 110855
Main Authors Zhao, Sangqi, Wei, Yian, Cheng, Yao, Li, Yang
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.05.2025
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Online AccessGet full text
ISSN0951-8320
DOI10.1016/j.ress.2025.110855

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Abstract Making optimal decisions for system size and its components’ replacement and spare parts’ replenishment is crucial for the normal operation of a k-out-of-n system whose components deteriorate over time. This task is challenging when the components have load-dependent deterioration rates and the system is subject to self-announcing failures. In this paper, we develop a Semi-Markov decision process (SMDP) based reinforcement learning (RL) framework for designing a joint size and state-specific maintenance and inventory policy for such systems. The proposed policy outperforms the threshold-based policies in minimizing the system's overall cost rate. First, we identify the form of the state-specific policy that determines the number of components to be replaced and replenishment levels at aperiodic decision epochs. Second, we develop an SMDP-based framework for modeling the state transition process and overall cost rate of the system. The structural properties of the optimal policy are obtained, based on which we narrow the search space. Last, we propose a Dueling Double Deep Q-Network (D3QN) algorithm with invalid action masking to alleviate the dimensionality explosion issue caused by the large search space. A case study of a feedwater pump system illustrates the proposed policy's efficiency.
AbstractList Making optimal decisions for system size and its components’ replacement and spare parts’ replenishment is crucial for the normal operation of a k-out-of-n system whose components deteriorate over time. This task is challenging when the components have load-dependent deterioration rates and the system is subject to self-announcing failures. In this paper, we develop a Semi-Markov decision process (SMDP) based reinforcement learning (RL) framework for designing a joint size and state-specific maintenance and inventory policy for such systems. The proposed policy outperforms the threshold-based policies in minimizing the system's overall cost rate. First, we identify the form of the state-specific policy that determines the number of components to be replaced and replenishment levels at aperiodic decision epochs. Second, we develop an SMDP-based framework for modeling the state transition process and overall cost rate of the system. The structural properties of the optimal policy are obtained, based on which we narrow the search space. Last, we propose a Dueling Double Deep Q-Network (D3QN) algorithm with invalid action masking to alleviate the dimensionality explosion issue caused by the large search space. A case study of a feedwater pump system illustrates the proposed policy's efficiency.
ArticleNumber 110855
Author Cheng, Yao
Zhao, Sangqi
Li, Yang
Wei, Yian
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Keywords Self-announcing failures
Dueling double deep Q-Network (D3QN) algorithm
Joint size, Maintenance, and Inventory Policy
State-specific policy
Load-sharing system
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Snippet Making optimal decisions for system size and its components’ replacement and spare parts’ replenishment is crucial for the normal operation of a k-out-of-n...
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SubjectTerms Dueling double deep Q-Network (D3QN) algorithm
Joint size, Maintenance, and Inventory Policy
Load-sharing system
Self-announcing failures
State-specific policy
Title A state-specific joint size, maintenance, and inventory policy for a k-out-of-n load-sharing system subject to self-announcing failures
URI https://dx.doi.org/10.1016/j.ress.2025.110855
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