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

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  • Jin, Haibo
  • Song, Xianhe
  • Xia, Hao

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.

Suggested Citation

  • Jin, Haibo & Song, Xianhe & Xia, Hao, 2023. "Optimal maintenance strategy for large-scale production systems under maintenance time uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    • Handle: RePEc:eee:reensy:v:240:y:2023:i:c:s0951832023005082
    DOI: 10.1016/j.ress.2023.109594
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    References listed on IDEAS

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    1. A. Jamali & E. Khaleghi & I. Gholaminezhad & N. Nariman-Zadeh & B. Gholaminia & A. Jamal-Omidi, 2017. "Multi-objective genetic programming approach for robust modeling of complex manufacturing processes having probabilistic uncertainty in experimental data," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 149-163, January.
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    5. Heydar, Mojtaba & Mardaneh, Elham & Loxton, Ryan, 2022. "Approximate dynamic programming for an energy-efficient parallel machine scheduling problem," European Journal of Operational Research, Elsevier, vol. 302(1), pages 363-380.
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