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Joint optimization of ordering and maintenance with condition monitoring data

Author

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  • Ramin Moghaddass

    (University of Miami)

  • Şeyda Ertekin

    (Middle East Technical University
    Massachusetts Institute of Technology)

Abstract

We study a single-unit deteriorating system under condition monitoring for which collected signals are only stochastically related to the actual level of degradation. Failure replacement is costlier than preventive replacement and there is a delay (lead time) between the initiation of the maintenance setup and the actual maintenance, which is closely related to the process of spare parts inventory and/or maintenance setup activities. We develop a dynamic control policy with a two-dimensional decision space, referred to as a warning-replacement policy, which jointly optimizes the replacement time and replacement setup initiation point (maintenance ordering time) using online condition monitoring data. The optimization criterion is the long-run expected average cost per unit of operation time. We develop the optimal structure of such a dynamic policy using a partially observable semi-Markov decision process and provide some important results with respect to optimality and monotone properties of the optimal policy. We also discuss how to find the optimal values of observation/inspection interval and lead time using historical condition monitoring data. Illustrative numerical examples are provided to show thatour joint policy outperforms conventional suboptimal policies commonly used in theliterature.

Suggested Citation

  • Ramin Moghaddass & Şeyda Ertekin, 2018. "Joint optimization of ordering and maintenance with condition monitoring data," Annals of Operations Research, Springer, vol. 263(1), pages 271-310, April.
    • Handle: RePEc:spr:annopr:v:263:y:2018:i:1:d:10.1007_s10479-017-2745-3
    DOI: 10.1007/s10479-017-2745-3
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    References listed on IDEAS

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    Cited by:

    1. Reza Ahmadi, 2024. "Reliability and maintenance modeling for a production system by means of point process observations," Annals of Operations Research, Springer, vol. 340(1), pages 3-26, September.

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