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基于改进遗传算法的长期飞机检修规划编排
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Abstract:
长期飞机检修计划编排问题根据飞机的检修参数与维修能力，为机队指定3~5年内的大型检修计划。大型检修计划作为飞机维护运营计划的基础，侧重于满足各类维修资源限制并尽可能减少维修次数，有利于航空公司合理分配飞机检修时间安排，以保障航班正常运行，提高机组利用率，增加运营收入。由于时间周期较长，约束较为复杂，提出了一种遗传算法结合邻域搜索算子的启发式算法，实验结果表明，GA-NS算法在解决大规模问题方面表现出了较好的效率和准确性，为航空业提供了更科学的维修计划设计方法，有望提高服务质量，提升运营效率。
The long-term aircraft maintenance planning problem involves specifying a comprehensive maintenance schedule for a fleet over 3~5 years, considering aircraft maintenance parameters and capabilities. This planning, as the foundation of aircraft maintenance operations, focuses on meeting various maintenance resource constraints while minimizing the number of maintenance events. It aids airlines in efficiently allocating aircraft maintenance schedules to ensure normal flight operations, enhance crew utilization, and increase operational revenue. Given the extended planning horizon and complex constraints, a heuristic algorithm combining a genetic algorithm with neighborhood search operators, GA-NS, is proposed. Experimental results demonstrate that the GA-NS algorithm exhibits high efficiency and accuracy in addressing large-scale problems. It offers the aviation industry a more scientifically informed approach to maintenance planning, with the potential to enhance service quality and operational efficiency.
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