多机协同定检智能排班优化方法研究

doi:10.16097/j.cnki.1009-6744.2026.02.014

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (2): 148-158.DOI: 10.16097/j.cnki.1009-6744.2026.02.014

• 智能交通系统与信息技术 • 上一篇下一篇

多机协同定检智能排班优化方法研究

胡小兵*，那容菲，李航

中国民航大学，安全科学与工程学院，天津300300

收稿日期:2025-09-17 修回日期:2026-02-16 接受日期:2026-03-19 出版日期:2026-04-25 发布日期:2026-04-20
作者简介:胡小兵（1975—），男，四川攀枝花人，教授，博士。
基金资助:
国家自然科学基金(62541103)；中央高校基本科研业务费项目 (3122026012)。

Intelligent Scheduling Optimization for Multi-aircraft Collaborative Maintenance

HU Xiaobing^*, NA Rongfei, LI Hang

College of Safety Science and Engineering, Civil Aviation University of China, Tianjin 300300, China

Received:2025-09-17 Revised:2026-02-16 Accepted:2026-03-19 Online:2026-04-25 Published:2026-04-20
Supported by:
National Natural Science Foundation of China(62541103)；Fundamental Research Funds for the Central Universities (3122026012)。

摘要/Abstract

摘要： 针对多架飞机协同定检排班优化问题，为减少飞机停场时间，实现资源合理分配，本文提出一种基于启发式策略的优化算法。考虑复杂约束条件限制，建立一个多机协同定检排班问题的数学模型，设定最小化资源使用量期望值与实际值的差值，并引入飞机优先级的加权值作为目标优化函数。该算法在共享资源池中基于遗传算法，结合多机定检维修任务的特点，在构建初始种群、染色体编码、种群选择、交叉、变异操作方面进行定制化设计，以期得到优化调度排班结果。选取3架B737-800飞机真实的C检数据作为实验对象，对不同方法下的多机协同定检排班优化进行模拟仿真实验和对比分析。实验结果表明，在共享资源池中用遗传算法对多机协同定检排班能够显著优化各类维修人员的资源使用量。相较于人工手动排班方法，所提算法的方差减少率均在76.43%以上，关键定检阶段工期从11d缩短至9d。

关键词: 航空运输, 飞机定检维修排班问题, 遗传算法, 资源受限项目调度问题, 启发式算法

Abstract: To optimize the collaborative maintenance scheduling for multiple aircrafts, a heuristic-based algorithm is proposed to reduce the maintenance time and achieve the balanced allocation of resources. Considering the complex constraints, a mathematical model is established to minimize the deviation between the expected and actual resource utilization, while it incorporates the weighted aircraft priorities into the objective function. Within a shared resource pool, the algorithm is developed on a genetic framework and customized in population initialization, chromosome encoding, selection, crossover, and mutation to generate optimized schedules. Real C-check data from three B737-800 aircrafts are used for a simulation and comparative analysis. The proposed GA method significantly improves the resource utilization under a shared resource pool. Compared with manual scheduling, it reduces variance by over 76.43% and shortens the critical maintenance duration from 11 to 9 days.

Key words: air transportation, aircraft maintenance scheduling problem, genetic algorithm, resource-constrained project scheduling problem, heuristic algorithm

中图分类号:

胡小兵, 那容菲, 李航. 多机协同定检智能排班优化方法研究[J]. 交通运输系统工程与信息, 2026, 26(2): 148-158.

HU Xiaobing, NA Rongfei, LI Hang. Intelligent Scheduling Optimization for Multi-aircraft Collaborative Maintenance[J]. Journal of Transportation Systems Engineering and Information Technology, 2026, 26(2): 148-158.

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http://www.tseit.org.cn/CN/Y2026/V26/I2/148

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多机协同定检智能排班优化方法研究

Intelligent Scheduling Optimization for Multi-aircraft Collaborative Maintenance

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