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王 博,王剑辉,彭笑非,等. 基于机场协同决策系统的机坪牵引车调度方法[J]. 科学技术与工程, 2021, 21(4): 1667-1673.
WangBo,et al.The Method for Scheduling Towing TractorsBased on A-CDM System[J].Science Technology and Engineering,2021,21(4):1667-1673.
基于机场协同决策系统的机坪牵引车调度方法
The Method for Scheduling Towing TractorsBased on A-CDM System
投稿时间:2020-04-30  修订日期:2020-11-17
DOI:
中文关键词:  机场协同决策系统  机坪保障作业  牵引车调度  多目标遗传算法  可视化程序
英文关键词:airport collaborative decision making system  the apron service  scheduling of towing tractors  multi-objective genetic algorithm  visualized program
基金项目:民航局安全能力建设项目(2020138)
           
作者单位
王 博 中国民航飞行学院 空中交通管理学院
王剑辉 中国民航飞行学院 空中交通管理学院
彭笑非 中国民用航空总局第二研究所 民航空管工程技术研究所
苏 刚 中国民用航空总局第二研究所 民航空管工程技术研究所
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中文摘要:
      为优化机坪牵引车服务流程,将机场协同决策系统与数学模型、多目标遗传算法相结合,通过理论计算与实例验证的方法研究了牵引车调度优化问题。首先,利用机场协同决策系统获取航空器的预计推出时刻;再基于历史运行数据设计航空器推出耗时和牵引车行驶耗时的生成方式,考虑航空器推出过程中可能引发的推出冲突设置约束条件,建立以某一时段、机坪单一保障任区内航空器推出作业总费用最小、参与作业的牵引车数量最少和牵引车服务的航空器数量保持均衡为优化目标的数学模型;然后,针对性地设计多目标遗传算法,并结合实例进行验证。结果表明:提出的机坪牵引车调度方法相较于传统的人工调度方案而言,推出作业总费用降低31.67%,服务航班数方差降至0,方案制定时间缩短71.15%。可见该调度方法能明显优化牵引车的服务流程。最后,在该方法的基础上设计了可视化的牵引车调度程序,为一线运营人员提供决策参考。
英文摘要:
      In order to optimize the service process of towing tractors, the problem of tractors dispatching optimization is studied through theoretical calculation and case verification. It combined with Airport Collaborative Decision Making (A-CDM) system, mathematical model and multi-objective genetic algorithm. Firstly, the plan time of Push-Back of each aircraft was obtained by the A-CDM system. And the time-consuming of operations and tractors travelling was designed based on the historical operation data. Constraints were set based on the conflicts during the operation of Push-Back. The mathematical model in a single area within a certain period of time was established. Objective function included three parts: the smallest total cost caused by the Push-Back service, the smallest number of tractors participating in the operation and the balanced tasks distribution. Then, the multi-objective genetic algorithm was designed and verified by an example. The results show that the proposed method has been significantly optimized compared with the traditional manual scheduling model. The total cost of operations is reduced by 31.67%, the variance of the missions is reduced to zero, and the formulation time of scheme is shortened by 71.15%. Finally, a visualized program of scheduling tractors is designed, which provides decision-making reference for airport operators.
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