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Maintenance scheduling of a manufacturing system subject to deterioration

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  • Ahmadi, Reza
  • Newby, Martin

Abstract

This paper presents an integrated model for the joint determination of both optimal inspection strategy and optimal repair policy for a manufacturing system whose resulting output is subject to system state. An appropriate maintenance strategy is essential to optimize revenue from a manufacturing system which is in continuous operation and subject to deterioration. The optimum policy balances the amount of maintenance required to increase availability against the loss of revenue arising from the down time: insufficient maintenance leads to an increase in the number of defective items, low profit and low maintenance cost; excessive maintenance results in a reduction in the proportion of defective items, high profit and high maintenance cost. In this paper, an intensity control model adapted to partial information provides an optimal inspection intensity and repair degree of the system as an optimal control process to yield maximum revenue. The solution is obtained through formulating an equivalent deterministic Hamilton–Jacobi equation. A numerical example is provided to illustrate the behavior of the optimal control process. The optimal control process determines a solution to both optimum inspection frequency and optimal replacement policy which results in an optimal production run length of the system.

Suggested Citation

  • Ahmadi, Reza & Newby, Martin, 2011. "Maintenance scheduling of a manufacturing system subject to deterioration," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1411-1420.
    • Handle: RePEc:eee:reensy:v:96:y:2011:i:10:p:1411-1420
    DOI: 10.1016/j.ress.2011.05.004
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Héctor Rivera-Gómez & Jorge Lara & Oscar Montaño-Arango & Eva Selene Hernández-Gress & José Ramón Corona-Armenta & Francisca Santana-Robles, 2019. "Joint production and repair efficiency planning of a multiple deteriorating system," Flexible Services and Manufacturing Journal, Springer, vol. 31(2), pages 446-471, June.
    2. Salvatore T. March & Gary D. Scudder, 2019. "Predictive maintenance: strategic use of IT in manufacturing organizations," Information Systems Frontiers, Springer, vol. 21(2), pages 327-341, April.
    3. Ahmadi, Reza & Wu, Shaomin, 2017. "A novel data-driven approach to optimizing replacement policy," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 506-516.
    4. Zhou, Xiaojun & Lu, Zhiqiang & Xi, Lifeng, 2012. "Preventive maintenance optimization for a multi-component system under changing job shop schedule," Reliability Engineering and System Safety, Elsevier, vol. 101(C), pages 14-20.
    5. Sinisterra, Wilfrido Quiñones & Lima, Victor Hugo Resende & Cavalcante, Cristiano Alexandre Virginio & Aribisala, Adetoye Ayokunle, 2023. "A delay-time model to integrate the sequence of resumable jobs, inspection policy, and quality for a single-component system," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    6. Ahmadi, Reza & Fouladirad, Mitra, 2017. "Maintenance planning for a deteriorating production process," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 108-118.
    7. Reza Ahmadi, 2014. "Optimal maintenance scheduling for a complex manufacturing system subject to deterioration," Annals of Operations Research, Springer, vol. 217(1), pages 1-29, June.
    8. Chakravarthy, Srinivas R., 2012. "Maintenance of a deteriorating single server system with Markovian arrivals and random shocks," European Journal of Operational Research, Elsevier, vol. 222(3), pages 508-522.
    9. Salvatore T. March & Gary D. Scudder, 0. "Predictive maintenance: strategic use of IT in manufacturing organizations," Information Systems Frontiers, Springer, vol. 0, pages 1-15.

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