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Maintenance optimization for a Markovian deteriorating system with population heterogeneity

Author

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  • Chiel van Oosterom
  • Hao Peng
  • Geert-Jan van Houtum

Abstract

We develop a partially observable Markov decision process model to incorporate population heterogeneity when scheduling replacements for a deteriorating system. The single-component system deteriorates over a finite set of condition states according to a Markov chain. The population of spare components that is available for replacements is composed of multiple component types that cannot be distinguished by their exterior appearance but deteriorate according to different transition probability matrices. This situation may arise, for example, because of variations in the production process of components. We provide a set of conditions for which we characterize the structure of the optimal policy that minimizes the total expected discounted operating and replacement cost over an infinite horizon. In a numerical experiment, we benchmark the optimal policy against a heuristic policy that neglects population heterogeneity.

Suggested Citation

  • Chiel van Oosterom & Hao Peng & Geert-Jan van Houtum, 2017. "Maintenance optimization for a Markovian deteriorating system with population heterogeneity," IISE Transactions, Taylor & Francis Journals, vol. 49(1), pages 96-109, January.
    • Handle: RePEc:taf:uiiexx:v:49:y:2017:i:1:p:96-109
    DOI: 10.1080/0740817X.2016.1205239
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    Citations

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    Cited by:

    1. Dursun, İpek & Akçay, Alp & van Houtum, Geert-Jan, 2022. "Age-based maintenance under population heterogeneity: Optimal exploration and exploitation," European Journal of Operational Research, Elsevier, vol. 301(3), pages 1007-1020.
    2. David T. Abdul‐Malak & Jeffrey P. Kharoufeh & Lisa M. Maillart, 2019. "Maintaining systems with heterogeneous spare parts," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(6), pages 485-501, September.
    3. Zhao, Xiujie & Liu, Bin & Xu, Jianyu & Wang, Xiao-Lin, 2023. "Imperfect maintenance policies for warranted products under stochastic performance degradation," European Journal of Operational Research, Elsevier, vol. 308(1), pages 150-165.
    4. Deep, Akash & Zhou, Shiyu & Veeramani, Dharmaraj & Chen, Yong, 2023. "Partially observable Markov decision process-based optimal maintenance planning with time-dependent observations," European Journal of Operational Research, Elsevier, vol. 311(2), pages 533-544.
    5. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    6. Dursun, İpek & Akçay, Alp & van Houtum, Geert-Jan, 2022. "Data pooling for multiple single-component systems under population heterogeneity," International Journal of Production Economics, Elsevier, vol. 250(C).
    7. Cheng, Yao & Wei, Yian & Liao, Haitao, 2022. "Optimal sampling-based sequential inspection and maintenance plans for a heterogeneous product with competing failure modes," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    8. de Jonge, Bram, 2019. "Discretizing continuous-time continuous-state deterioration processes, with an application to condition-based maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 1-5.
    9. Cai, Yue & Teunter, Ruud H. & de Jonge, Bram, 2023. "A data-driven approach for condition-based maintenance optimization," European Journal of Operational Research, Elsevier, vol. 311(2), pages 730-738.
    10. Lu, Biao & Chen, Zhen & Zhao, Xufeng, 2021. "Data-driven dynamic predictive maintenance for a manufacturing system with quality deterioration and online sensors," Reliability Engineering and System Safety, Elsevier, vol. 212(C).

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