Joint optimization of maintenance and spare parts provisioning policies for multi-state protection systems considering mission failures

Condition-based maintenance (CBM) and spare parts provisioning for protection systems are critical for ensuring the safety and operational stability of complex infrastructure, such as electrical systems, oil pipelines, and nuclear reactors. However, current research often overlooks the impact of mission failures in protection systems on maintenance decision-making. Moreover, most existing studies on the joint optimization of CBM and spare parts inventory management typically consider only a single spare parts supply mode. To address these limitations, this study employs stochastic processes to model the failure dynamics of the protection system and the execution of maintenance tasks. Based on this framework, we formulate corresponding maintenance strategies and spare parts provisioning strategies tailored to various inspection outcomes and mission states. These strategies incorporate the correlation between system health conditions and mission execution capability, as well as the time delays inherent in regular spare parts supply and the rapid availability of emergency supply options. To deal with the resulting optimization problem, we present a hybrid algorithm that integrates the Artificial Bee Colony (ABC) algorithm with Differential Evolution (DE), termed the ABC-DE algorithm. Comparative experimental results show that the ABC-DE algorithm improves solution accuracy rate by 33.3%, 33.3%, and 300% compared to the traditional ABC, DE, and Particle Swarm Optimization (PSO) algorithms, respectively, and significantly surpasses the performance of the Genetic Algorithm (GA). Furthermore, numerical case studies and sensitivity analyses confirm that the long-term expected cost rate derived from the proposed model is consistently lower than those obtained from Models 2 and 3. Therefore, the findings provide valuable managerial insights for decision-makers involved in protection system maintenance and logistics planning.