A high-dimensional many-objective co-optimization method on equipment utilization and maintenance

In order to enhance the utilization and maintenance efficiency of equipment, grounded in the theory of preventive maintenance, the interrelationship between equipment usage and maintenance was analyzed. By proposing optimization objectives aimed at reducing the mean failure rate, preventive maintenance time, standard deviation of monthly motor-hour reserve, and maintenance costs, and comprehensively incorporating both preventive and corrective maintenance strategies, an optimization model for equipment utilization and maintenance was established. A DNA-based population encoding method, along with crossover and mutation operations was designed. Furthermore, an adaptive reinforcement learning algorithm was employed to adjust the reference vectors, and the NSGA-III algorithm is improved for simulation experiments. The model and algorithm are not only practical but also computationally efficient, scientifically sound, and applicable. Upon analyzing the simulation results, optimization strategies for equipment utilization preferences were proposed. These strategies can provide decision-making support for developing equipment deployment and maintenance plans for army.