Joint optimization of condition-based maintenance and condition-based production of a single equipment considering random yield and maintenance delay

Zhang, Nan; Cai, Kaiquan; Deng, Yingjun; Zhang, Jun

doi:10.1016/j.ress.2023.109694

Reliability Engineering & System Safety

2024

DOI: 10.1016/j.ress.2023.109694

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Joint optimization of condition-based maintenance and condition-based production of a single equipment considering random yield and maintenance delay

Nan Zhang,

Kaiquan Cai,

Yingjun Deng

et al.

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2024

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Cited by 11 publications

References 37 publications

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Joint Optimization of Condition‐Based Maintenance and Production Rate Using Reinforcement Learning Algorithms

Rasay,

Azizi,

Salmani

et al. 2024

Quality & Reliability Eng

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Maintenance has always been a crucial aspect of the manufacturing and industrial sectors. There is a recent surge of interest in utilizing advanced machine learning, and reinforcement learning models to enhance maintenance strategies. In this regard, this paper focuses on the development of a joint optimization model of maintenance and production for a special type of production system that has an adjustable production rate, where the system's deterioration is closely related to the production rate. When the production rate is increased, the expected deterioration of the system also increases. To control the deterioration of the system, the paper proposes two main actions or policies: maintenance policy and production policy. These policies involve scheduling and conducting maintenance actions on the system and adjusting the production rate, respectively. To solve the optimization problem of minimizing the expected costs of the system during a finite planning horizon, the paper develops a Markov decision process and employs reinforcement learning algorithms such as Q‐learning and SARSA. The hyperparameters of the algorithms are tuned using a value‐iteration algorithm of dynamic programming. The developed optimal actions given the state of the system ensure efficient management of the production system while controlling the deterioration of the system.

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Joint Optimization of Condition‐Based Maintenance and Production Rate Using Reinforcement Learning Algorithms

Rasay,

Azizi,

Salmani

et al. 2024

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

Artificial Intelligence-Based Recommendation System for Detecting and Diagnosing Broken Bars in Induction Motors Under Transient Operation

Maciejewski,

Freire,

Szejka

et al. 2024

IFAC-PapersOnLine

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A hybrid deep learning approach to integrate predictive maintenance and production planning for multi-state systems

Dehghan Shoorkand,

Nourelfath,

Hajji

2024

Journal of Manufacturing Systems

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Joint optimization of condition-based maintenance and condition-based production of a single equipment considering random yield and maintenance delay

Cited by 11 publications

References 37 publications

Joint Optimization of Condition‐Based Maintenance and Production Rate Using Reinforcement Learning Algorithms

Joint Optimization of Condition‐Based Maintenance and Production Rate Using Reinforcement Learning Algorithms

Artificial Intelligence-Based Recommendation System for Detecting and Diagnosing Broken Bars in Induction Motors Under Transient Operation

A hybrid deep learning approach to integrate predictive maintenance and production planning for multi-state systems

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