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Simultaneous optimization of repair and control-limit policy in condition-based maintenance

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  • Sayyideh Mehri Mousavi

    (Tafresh University)

  • Hesam Shams

    (University of Tennessee)

  • Shahrzad Ahmadi

    (University of Houston)

Abstract

In condition-based maintenance (CBM) planning, collected information from system condition monitoring is the basis of making decision about conducting the maintenance and repair activities. Recently, ample number of studies has been conducted in CBM field especially, in control-limit policy. In control-limit policy, using proportional Hazards model and results of monitoring system condition, one can estimate hazard rate function and its condition’s transition probability matrix. Then, considering replacement costs, optimal control-limit can be determined minimizing the average cost in the long run. The presented model considers repair policy and their implementation cost, and the assumptions of repair during interval inspection is ignored. Then, a model is presented to determine the optimal control-limit and the best repair policy, in which the average total cost per unit time in the long-run, is minimized. At the end, a numerical example is illustrated.

Suggested Citation

  • Sayyideh Mehri Mousavi & Hesam Shams & Shahrzad Ahmadi, 2017. "Simultaneous optimization of repair and control-limit policy in condition-based maintenance," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 245-254, January.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:1:d:10.1007_s10845-014-0974-8
    DOI: 10.1007/s10845-014-0974-8
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    References listed on IDEAS

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    1. Love, C. E. & Zhang, Z. G. & Zitron, M. A. & Guo, R., 2000. "A discrete semi-Markov decision model to determine the optimal repair/replacement policy under general repairs," European Journal of Operational Research, Elsevier, vol. 125(2), pages 398-409, September.
    2. Chen, Dongyan & Trivedi, Kishor S., 2005. "Optimization for condition-based maintenance with semi-Markov decision process," Reliability Engineering and System Safety, Elsevier, vol. 90(1), pages 25-29.
    3. V. Makis & X. Jiang & K. Cheng, 2000. "Optimal Preventive Replacement Under Minimal Repair and Random Repair Cost," Mathematics of Operations Research, INFORMS, vol. 25(1), pages 141-156, February.
    4. W Wang, 2003. "Modelling condition monitoring intervals: A hybrid of simulation and analytical approaches," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(3), pages 273-282, March.
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    Cited by:

    1. Najafi, Seyedvahid & Zheng, Rui & Lee, Chi-Guhn, 2021. "An optimal opportunistic maintenance policy for a two-unit series system with general repair using proportional hazards models," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. A. S. Xanthopoulos & S. Vlastos & D. E. Koulouriotis, 2022. "Coordinating production, inspection and maintenance decisions in a stochastic manufacturing system with deterioration failures," Operational Research, Springer, vol. 22(5), pages 5707-5732, November.
    3. Edson Ruschel & Eduardo Alves Portela Santos & Eduardo de Freitas Rocha Loures, 2020. "Establishment of maintenance inspection intervals: an application of process mining techniques in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 53-72, January.
    4. Zheng, Rui & Chen, Bingkun & Gu, Liudong, 2020. "Condition-based maintenance with dynamic thresholds for a system using the proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    5. Zheng, Rui & Zhou, Yifan, 2021. "Comparison of three preventive maintenance warranty policies for products deteriorating with age and a time-varying covariate," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    6. Zheng, Rui & Wang, Jingjing & Zhang, Yingzhi, 2023. "A hybrid repair-replacement policy in the proportional hazards model," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1011-1021.

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