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Optimal preventive maintenance policy based on reinforcement learning of a fleet of military trucks

Author

Listed:
  • Stephane R. A. Barde

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Soumaya Yacout

    (Ecole Polytechnique de Montreal)

  • Hayong Shin

    (Korea Advanced Institute of Science and Technology (KAIST))

Abstract

In this paper, we model preventive maintenance strategies for equipment composed of multi-non-identical components which have different time-to-failure probability distribution, by using a Markov decision process (MDP). The originality of this paper resides in the fact that a Monte Carlo reinforcement learning (MCRL) approach is used to find the optimal policy for each different strategy. The approach is applied to an already existing published application which deals with a fleet of military trucks. The fleet consists of a group of similar trucks that are composed of non-identical components. The problem is formulated as a MDP and solved by a MCRL technique. The advantage of this modeling technique when compared to the published one is that there is no need to estimate the main parameters of the model, for example the estimation of the transition probabilities. These parameters are treated as variables and they are found by the modeling technique, while searching for the optimal solution. Moreover, the technique is not bounded by any explicit mathematical formula, and it converges to the optimal solution whereas the previous model optimizes the replacement policy of each component separately, which leads to a local optimization. The results show that by using the reinforcement learning approach, we are able of getting a 36.44 % better solution that is less downtime.

Suggested Citation

  • Stephane R. A. Barde & Soumaya Yacout & Hayong Shin, 2019. "Optimal preventive maintenance policy based on reinforcement learning of a fleet of military trucks," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 147-161, January.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:1:d:10.1007_s10845-016-1237-7
    DOI: 10.1007/s10845-016-1237-7
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    References listed on IDEAS

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    1. Gosavi, Abhijit, 2004. "Reinforcement learning for long-run average cost," European Journal of Operational Research, Elsevier, vol. 155(3), pages 654-674, June.
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    Cited by:

    1. Barlow, E. & Bedford, T. & Revie, M. & Tan, J. & Walls, L., 2021. "A performance-centred approach to optimising maintenance of complex systems," European Journal of Operational Research, Elsevier, vol. 292(2), pages 579-595.
    2. Wu, Tianyi & Yang, Li & Ma, Xiaobing & Zhang, Zihan & Zhao, Yu, 2020. "Dynamic maintenance strategy with iteratively updated group information," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    3. A. Khatab & C. Diallo & E.-H. Aghezzaf & U. Venkatadri, 2022. "Optimization of the integrated fleet-level imperfect selective maintenance and repairpersons assignment problem," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 703-718, March.
    4. Michele Compare & Luca Bellani & Enrico Cobelli & Enrico Zio & Francesco Annunziata & Fausto Carlevaro & Marzia Sepe, 2020. "A reinforcement learning approach to optimal part flow management for gas turbine maintenance," Journal of Risk and Reliability, , vol. 234(1), pages 52-62, February.
    5. Jorge Ribeiro & Pedro Andrade & Manuel Carvalho & Catarina Silva & Bernardete Ribeiro & Licínio Roque, 2022. "Playful Probes for Design Interaction with Machine Learning: A Tool for Aircraft Condition-Based Maintenance Planning and Visualisation," Mathematics, MDPI, vol. 10(9), pages 1-20, May.
    6. Ashish Kumar & Roussos Dimitrakopoulos & Marco Maulen, 2020. "Adaptive self-learning mechanisms for updating short-term production decisions in an industrial mining complex," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1795-1811, October.
    7. Correa-Jullian, Camila & López Droguett, Enrique & Cardemil, José Miguel, 2020. "Operation scheduling in a solar thermal system: A reinforcement learning-based framework," Applied Energy, Elsevier, vol. 268(C).

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