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Single-stage Kanban system with deterioration failures and condition-based preventive maintenance

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  • Xanthopoulos, A.S.
  • Koulouriotis, D.E.
  • Botsaris, P.N.

Abstract

Despite the fact that the fields of pull type production control policies and condition-based preventive maintenance have much in common contextually, they have evolved independently up to now. In this investigation, an attempt is made to bridge the gap between these two branches of knowledge by introducing the single-stage Kanban system with deterioration failures and condition-based preventive maintenance. The formalism of continuous time Markov chains is used to model the system and expressions for eight performance metrics are derived. Two important, from a managerial perspective, constrained optimization problems for the proposed model are defined where the objective is the simultaneous optimization of the Kanban policy, the preventive maintenance policy and the inspection schedule under conflicting performance criteria. Multiple instances of each optimization problem are solved by means of the augmented Lagrangian genetic algorithm. The results from the optimization trials coupled by the results from extensive numerical examples facilitate the thorough investigation of the system’s behaviour.

Suggested Citation

  • Xanthopoulos, A.S. & Koulouriotis, D.E. & Botsaris, P.N., 2015. "Single-stage Kanban system with deterioration failures and condition-based preventive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 111-122.
  • Handle: RePEc:eee:reensy:v:142:y:2015:i:c:p:111-122
    DOI: 10.1016/j.ress.2015.05.008
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    References listed on IDEAS

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    1. Panagiotidou, S. & Tagaras, G., 2012. "Optimal integrated process control and maintenance under general deterioration," Reliability Engineering and System Safety, Elsevier, vol. 104(C), pages 58-70.
    2. E. Iakovou & C.M. Ip & C. Koulamas, 1999. "Throughput‐dependent periodic maintenance policiesfor general production units," Annals of Operations Research, Springer, vol. 91(0), pages 41-47, January.
    3. Asadzadeh, S.M. & Azadeh, A., 2014. "An integrated systemic model for optimization of condition-based maintenance with human error," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 117-131.
    4. 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.
    5. Zhou, Xiaojun & Xi, Lifeng & Lee, Jay, 2007. "Reliability-centered predictive maintenance scheduling for a continuously monitored system subject to degradation," Reliability Engineering and System Safety, Elsevier, vol. 92(4), pages 530-534.
    6. Lavoie, P. & Gharbi, A. & Kenné, J.-P., 2010. "A comparative study of pull control mechanisms for unreliable homogenous transfer lines," International Journal of Production Economics, Elsevier, vol. 124(1), pages 241-251, March.
    7. Yu, Haiyang & Yalaoui, Farouk & Châtelet, Ėric & Chu, Chengbin, 2007. "Optimal design of a maintainable cold-standby system," Reliability Engineering and System Safety, Elsevier, vol. 92(1), pages 85-91.
    8. Saumil Ambani & Semyon Meerkov & Liang Zhang, 2010. "Feasibility and optimization of preventive maintenance in exponential machines and serial lines," IISE Transactions, Taylor & Francis Journals, vol. 42(10), pages 766-777.
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    Cited by:

    1. 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.
    2. Yang, Hongbing & Li, Wenchao & Wang, Bin, 2021. "Joint optimization of preventive maintenance and production scheduling for multi-state production systems based on reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    3. Yang Li & Qirong Tang & Qing Chang & Michael P. Brundage, 2017. "An event-based analysis of condition-based maintenance decision-making in multistage production systems," International Journal of Production Research, Taylor & Francis Journals, vol. 55(16), pages 4753-4764, August.
    4. Asefeh Hasani Goodarzi & Seyed Hessameddin Zegordi, 2020. "Vehicle routing problem in a kanban controlled supply chain system considering cross-docking strategy," Operational Research, Springer, vol. 20(4), pages 2397-2425, December.

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