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Production and replacement policies for a deteriorating manufacturing system under random demand and qualityAuthor-Name: Ouaret, Samir

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  • Kenné, Jean-Pierre
  • Gharbi, Ali

Abstract

This work investigates the production planning of an unreliable deteriorating manufacturing system under uncertainties. The effect of the deterioration phenomenon on the machine is mainly observed in its availability and the quality of the parts produced, with the rates of failure and defectives increasing with the age of the machine. The option to replace the machine should be considered to mitigate the effect of deterioration in order to ensure long-term satisfaction of demand. The objective of this paper is to find the production rate and the replacement policy that minimize the total discounted cost, which includes inventory, backlog, production, repair and replacement costs, over an infinite planning horizon. We formulate the stochastic control problem in the framework of a semi-Markov decision process to consider the machine's history. The integration of random demand and quality behaviour led us to propose a new modeling approach by developing optimality conditions in terms of a second-order approximation of Hamilton–Jacobi–Bellman (HJB) equations. Numerical methods are used to obtain the optimal control policies. Finally, a numerical example and a sensitivity analysis are presented in order to illustrate and confirm the structure of the optimal solution obtained.

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  • Kenné, Jean-Pierre & Gharbi, Ali, 2018. "Production and replacement policies for a deteriorating manufacturing system under random demand and qualityAuthor-Name: Ouaret, Samir," European Journal of Operational Research, Elsevier, vol. 264(2), pages 623-636.
    • Handle: RePEc:eee:ejores:v:264:y:2018:i:2:p:623-636
    DOI: 10.1016/j.ejor.2017.06.062
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    3. Delia Montoro-Cazorla & Rafael Pérez-Ocón, 2022. "Optimizing Costs in a Reliability System under Markovian Arrival of Failures and Reposition by K -Policy Inspection," Mathematics, MDPI, vol. 10(11), pages 1-21, June.
    4. Levitin, Gregory & Finkelstein, Maxim & Dai, Yuanshun, 2018. "Heterogeneous standby systems with shocks-driven preventive replacements," European Journal of Operational Research, Elsevier, vol. 266(3), pages 1189-1197.
    5. Shengli Lv, 2021. "Multi-Machine Repairable System with One Unreliable Server and Variable Repair Rate," Mathematics, MDPI, vol. 9(11), pages 1-16, June.
    6. 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.
    7. Wang, Lin & Lu, Zhiqiang & Ren, Yifei, 2020. "Joint production control and maintenance policy for a serial system with quality deterioration and stochastic demand," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    8. Rivera-Gómez, Héctor & Gharbi, Ali & Kenné, Jean-Pierre & Montaño-Arango, Oscar & Hernández-Gress, Eva Selene, 2018. "Subcontracting strategies with production and maintenance policies for a manufacturing system subject to progressive deterioration," International Journal of Production Economics, Elsevier, vol. 200(C), pages 103-118.
    9. Xiufang Zhang & Tangbin Xia & Ershun Pan & Yuqing Li, 2022. "Integrated optimization on production scheduling and imperfect preventive maintenance considering multi-degradation and learning-forgetting effects," Flexible Services and Manufacturing Journal, Springer, vol. 34(2), pages 451-482, June.
    10. Zhang, Qin & Fang, Zhigeng & Cai, Jiajia, 2021. "Preventive replacement policies with multiple missions and maintenance triggering approaches," Reliability Engineering and System Safety, Elsevier, vol. 213(C).

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