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Joint optimization of production and maintenance for a serial–parallel hybrid two-stage production system

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  • Shen, Yilan
  • Zhang, Xi
  • Shi, Leyuan

Abstract

Effective machine maintenance and job scheduling strategies are two critical interactive tasks to upgrade the production efficiency in manufacturing industries. In this study, we mainly consider the interaction between production scheduling and maintenance, while optimizing the job scheduling problem in a serial–parallel hybrid two-stage production system. We formulate the problem by introducing the stochastic and workload-ratio degradation, and the actual job completion time and maintenance costs can be modeled according to the degradation status of each machine. To solve this problem, a two-step opportunistic maintenance (OM) strategy is developed, and finally an adaptive random-key genetic algorithm (GA) is designed by incorporating the OM coefficients. Numerical studies have been conducted to validate the proposed approach, and the results indicate the effectiveness compared to three existing methods under varying machine degradation scenarios.

Suggested Citation

  • Shen, Yilan & Zhang, Xi & Shi, Leyuan, 2022. "Joint optimization of production and maintenance for a serial–parallel hybrid two-stage production system," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:reensy:v:226:y:2022:i:c:s0951832022002459
    DOI: 10.1016/j.ress.2022.108600
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    Cited by:

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    5. Jun Wang & Yuyang Wang & Yuqiang Fu, 2023. "Joint Optimization of Condition-Based Maintenance and Performance Control for Linear Multi-State Consecutively Connected Systems," Mathematics, MDPI, vol. 11(12), pages 1-19, June.

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