computational optimization of preventive maintenance schedules in repairable mechanical systems using nsga-ii

In this study, a computational framework is proposed for optimizing preventive maintenance scheduling in complex mechanical systems, with a focus on minimizing total maintenance costs while preserving system availability and mechanical reliability. the model incorporates multi-level maintenance actions—including inspection, repair, and component replacement—over a defined planning horizon. a nonlinear integer programming formulation is developed to capture cost elements such as random failure, repair, replacement, and planned downtime. to address the combinatorial complexity of the problem, a non-dominated sorting genetic algorithm ii (nsga-ii) is employed to generate near-optimal solutions. the proposed method is applied to a real-world cathodic protection system used in steel gas distribution networks, which are critical mechanical infrastructures subject to electrochemical degradation. results demonstrate a 36% reduction in total maintenance costs, highlighting the effectiveness of the model in improving asset performance and extending system life through optimized maintenance strategies.