Maintenance-driven multi-stage joint optimization considering spare parts production, distribution and imperfect maintenance

Proactive maintenance is widely recognized for enhancing equipment reliability and reducing downtime costs. However, its role in optimizing spare parts production and distribution decisions remains underexplored, thereby limiting efficient cross-domain resource utilization within the supply-demand system. This paper addresses this gap by studying a maintenance-driven multi-stage joint optimization problem (MMJOP), which integrates flexible spare parts production, multi-vehicle distribution, and imperfect maintenance. We propose an optimal imperfect maintenance strategy to link these cross-domain business activities precisely, and further develop a mathematical model aimed at minimizing energy consumption on the supply side and operational costs on the demand side. To solve the MMJOP, we design an enhanced non-dominated neighbor immune algorithm, featuring a customized initialization operator and a problem-specific local search operator. Additionally, a Q-learning mechanism is employed to automatically select the most appropriate key parameters in the proposed algorithm. Extensive experiments indicate that: (1) the proposed components greatly enhance QNNIA's search performance; and (2) the QNNIA outperforms four well-known comparison algorithms regarding computational optimality, convergence, distribution, and stability. More importantly, the proposed model yields significant economic value, i.e., saving operational costs by 49% with negligible impact on overall energy consumption, proving the necessity of cross-domain business cooperation and resource optimization in the high-end equipment industry.