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Condition-based switching, loading, and age-based maintenance policies for standby systems

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  • Zhao, Xian
  • Li, Rong
  • Han, He
  • Qiu, Qingan

Abstract

Standby techniques are widely incorporated in structural design to enhance the inherent reliability of systems. To further leverage the system performance during operation, decision-makers can adopt operational policies to manage system degradation. Specifically, at the system level, unit switching that dynamically determines the online unit contributes to avoiding unexpected shutdowns. At the unit level, adjusting load levels to manage the trade-off between condition degradation and revenue accumulation is crucial for maximizing profit. Additionally, adopting age-based maintenance as a tactical decision, which effectively facilitates the integration of maintenance resources, can be implemented to restore a degraded system. For instance, maintenance is typically scheduled at fixed moments for multi-generator power systems located in remote areas. In between maintenance moments, the proactive switching of generators can ensure uninterrupted output, and the adjustment of load levels for online generators helps to maximize output. Motivated by such engineering practices, this paper investigates condition-based switching, loading, and age-based maintenance policies for standby systems to maximize the expected profit rate in the long-run horizon. The problem is formulated as a Markov decision process. The structural properties of the control-limit switching and monotone loading policies are analyzed for easy policy implementation and efficient problem solutions. For comparative purposes, several heuristic policies are proposed and evaluated. Finally, numerical examples are presented to validate theoretical results and illustrate the superiority of the proposed risk control policy.

Suggested Citation

  • Zhao, Xian & Li, Rong & Han, He & Qiu, Qingan, 2025. "Condition-based switching, loading, and age-based maintenance policies for standby systems," European Journal of Operational Research, Elsevier, vol. 321(2), pages 565-585.
    • Handle: RePEc:eee:ejores:v:321:y:2025:i:2:p:565-585
    DOI: 10.1016/j.ejor.2024.09.014
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