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A reliability based integrated model of maintenance planning with quality control and production decision for improving operational performance

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  • Tambe, Pravin P.
  • Kulkarni, Makarand S.

Abstract

This paper presents an integrated planning between the three core functions of shop floor management; maintenance, production scheduling, and quality. The methodology is based on the conditional reliability of components and its effect on system operation. The objective is to minimize the system operation cost of the combined decision and investigate the cost-effectiveness of the integrated policy over the non-integrated planning. The integrated approach helps the decision-maker to find maintenance actions for individual components, production schedule and sampling parameters for process quality inspection. Mathematical models for integration and a flow chart for the combined methodology of the three functions are presented. Metaheuristic approaches, simulated annealing, and genetic algorithm are used for optimization. A simulation study at different levels of component degradation (service age) and schedule tightness factor is presented. Besides, we present an experimental analysis of varying model parameter values for the integrated planning and compare the results with the independent (non-integrated) planning approach. The results show the improved operational performance of the integrated planning and a lesser operating cost than the non-integrated planning. The proposed approach is dynamic as quality control parameters and production decisions will change as per the maintenance decision.

Suggested Citation

  • Tambe, Pravin P. & Kulkarni, Makarand S., 2022. "A reliability based integrated model of maintenance planning with quality control and production decision for improving operational performance," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:reensy:v:226:y:2022:i:c:s0951832022003143
    DOI: 10.1016/j.ress.2022.108681
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    References listed on IDEAS

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    Cited by:

    1. Dehghan Shoorkand, Hassan & Nourelfath, Mustapha & Hajji, Adnène, 2024. "A hybrid CNN-LSTM model for joint optimization of production and imperfect predictive maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    2. Jiang, Junwei & An, Youjun & Dong, Yuanfa & Hu, Jiawen & Li, Yinghe & Zhao, Ziye, 2023. "Integrated optimization of non-permutation flow shop scheduling and maintenance planning with variable processing speed," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    3. Boumallessa, Zeineb & Chouikhi, Houssam & Elleuch, Mounir & Bentaher, Hatem, 2023. "Modeling and optimizing the maintenance schedule using dynamic quality and machine condition monitors in an unreliable single production system," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    4. Ye, Zhenggeng & Cai, Zhiqiang & Yang, Hui & Si, Shubin & Zhou, Fuli, 2023. "Joint optimization of maintenance and quality inspection for manufacturing networks based on deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    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. Finkelstein, Maxim & Cha, Ji Hwan & Langston, Amy, 2023. "Improving classical optimal age-replacement policies for degrading items," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    7. Mocellin, Paolo & Pilenghi, Lisa, 2023. "Semi-quantitative approach to prioritize risk in industrial chemical plants aggregating safety, economics and ageing: A case study," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    8. Shi, Haohao & Zhang, Ji & Zio, Enrico & Zhao, Xufeng, 2023. "Opportunistic maintenance policies for multi-machine production systems with quality and availability improvement," Reliability Engineering and System Safety, Elsevier, vol. 234(C).

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