关键词: 装备维修, 维修决策, 任务调度, 多目标优化

Abstract: In complex centralized maintenance environments, how to quickly integrate decision-making and task scheduling processes, establish and solve related models, has gradually become an urgent need for equipment maintenance support. Equipment maintenance under the comprehensive fixed-point maintenance mode has problems such as numerous maintenance modes, complex maintenance constraints, and cumbersome maintenance decision-making. Factors such as multiple maintenance modes, time conflicts, spare parts quantity, maintenance time, and priority should be considered. Propose the decision of serial replacement parts, combined with selective maintenance decision, and combine the secondary use time and priority of maintenance equipment as maintenance benefits. With the goal of maximizing maintenance benefits and the number of maintenance equipment under limited time, establish a more comprehensive mathematical model. Design corresponding encoding and decoding methods to address the above issues, and propose the self-adaptive hybrid particle swarm optimization and genetic algorithm (SHPSO-GA). Verify the rationality of the model and algorithm through simulation experiments and indicator evaluation. Experiments have shown that SHPSO-GA improves the average Pareto optimal solution by 4.2% compared to other algorithms in 10 cases, and increases the convergence speed by 65.4%-83.7%. It effectively solving the time and resource conflicts in maintenance decision-making under fixed-point mode, providing a highly robust solution for rapid equipment repair and task scheduling in battlefield environments.

Key words: equipment maintenance, maintenance decision, task scheduling, multi-objective optimization