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Joint sequential decision of maintenance and spare parts inventory for multi-unit repairable systems

Author

Listed:
  • Xiaohong Zhang
  • Yuxin Li
  • Jianfei Zhang
  • Jie Gan
  • Yongfei Zhang
  • Juan Shen

Abstract

Joint decision-making for preventive maintenance and spare parts inventory in multi-component systems is crucial for industrial applications, especially as many expensive, complex equipments can be repaired and reused. This study investigates this joint decision-making using a discrete multi-state degradation model, focusing on the unique characteristics of repairable systems, including their structure and maintenance strategies. First, the operational interactions among production, maintenance, and inventory are analysed to derive state transition probability models for degradation and ordering processes. Subsequently, a sequential decision model is developed to minimise the total system cost, identifying preventive maintenance thresholds, inspection periods, and order batch sequences. To address the problem, a combination of global dynamic programming and genetic algorithms is employed. Numerical experiments with wind turbine spindles validate the decision model, demonstrating its effectiveness in addressing maintenance and inventory optimisation in repairable multi-unit systems while ensuring an optimal dynamic combination of decision variables.

Suggested Citation

  • Xiaohong Zhang & Yuxin Li & Jianfei Zhang & Jie Gan & Yongfei Zhang & Juan Shen, 2025. "Joint sequential decision of maintenance and spare parts inventory for multi-unit repairable systems," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 19(1), pages 22-59.
    • Handle: RePEc:ids:ijrsaf:v:19:y:2025:i:1:p:22-59
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