Reliability‐Oriented Multi‐Machine Preventive Maintenance and Replacement Scheduling Under Degradation Dynamics

ABSTRACT This study investigates a multi‐machine, multi‐period preventive maintenance and replacement scheduling problem under reliability deterioration. Each machine is modeled as a repairable system whose degradation follows a power‐law process, while maintenance actions influence future reliability through an effective‐age mechanism. The proposed model simultaneously minimizes total operational cost, cumulative expected failures, and availability loss, resulting in a complex multi‐objective and combinatorial optimization problem. A comprehensive optimization framework is developed for integrated maintenance and replacement planning across multiple machines and periods. A unified scheduling representation is adopted to coordinate maintenance decisions under heterogeneous machine conditions and operational resource constraints, including crew capacity and maintenance policy restrictions. The framework further supports large‐scale industrial applications through flexible parameter settings and automatic data generation mechanisms. Computational experiments under various maintenance scenarios demonstrate the effectiveness of the proposed framework in generating high‐quality scheduling solutions and balancing economic performance, system reliability, and operational availability. Sensitivity analyses are conducted to evaluate the impacts of failure cost, maintenance effectiveness, and resource capacity on scheduling performance. The results indicate that the proposed framework provides a flexible and scalable decision‐support tool for maintenance optimization in complex industrial systems.