基于多目标融合及改进遗传算法的终端区进场协同排序
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作者单位:

1.中国民用航空飞行学院;2.中国民用航空飞行学

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中图分类号:

V355

基金项目:

中国民用航空飞行学院科研项目(J2021-082);


Collaborative Sequencing of Arrival Flights in Terminal Area Based on Multi-Objective Fusion and Improved Genetic Algorithm
Author:
Affiliation:

Civil Aviation Flight University of China

Fund Project:

Scientific research project of China Civil Aviation Flight Academy(J2021-082);

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    摘要:

    未来我国终端区将逐年递增,根据数据显示终端区内空域资源与飞行流量的增长不成正比,终端区内流量趋于饱和。为了有效提升终端区运行的效率,确保航空器在其空域的安全飞行,降低管制员的负荷以及公司的运行成本,本文从航空器延误,管制员负荷以及各机场资源平衡三个方向建立多机场终端区航空器进场协同排序模型。首先,通过对终端区范围界定,运行主要问题的研究以及空域结构的分析了解终端区系统的相互关联因素;其次,通过对终端区进场航空器的线路、交叉点的单独分析,找到相应的共同点和影响较高的运行系统相关性因素,相关的约束以及主要的解决目标;最后利用结合模拟退火的NSGA-II算法对该模型进行求解。结果表明基于改进遗传算法对该模型求解后对比先到先服务模式以及未改进的遗传算法在效率上分别提高26.3%和53.2%。由此可见,所提出的模型能有效的提高航空器排序的效率。

    Abstract:

    In the future,the number of the terminal airspace in China will increase year by year, according to the data, the airspace resources in the terminal area are not proportional to the growth of flight flow, and the flow in the terminal area tends to be saturated. In order to enhance the operation efficiency of terminal area more effectively, the safety of aircraft in its airspace is ensured, the load of controllers and the cost of company operation are reduced,A multi airport terminal area Aircraft Arrival collaborative sequencing model is established from three directions: aircraft delay, controller load and resource balance of each airport. Firstly, the interrelated factors of the terminal area system are obtained through the study of the scope of the terminal area, the main problems of operation and the analysis of the airspace structure; Then the corresponding commonalities, high impact operation system correlation factors, relevant constraints and main solution objectives are obtained through the separate analysis of the routes and intersections of aircraft entering the terminal area; Finally, the model is solved by NSGA-II algorithm combined with simulated annealing. The results show that the solution of the model based on the improved genetic algorithm is compared with the first come first serve model and the unmodified genetic algorithm, the efficiency is improved by 26.3% and 53.2% respectively. It can be seen that the efficiency of aircraft sequencing can be greatly improved through the proposed model.

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向 征,袁博轩,刘玥琳. 基于多目标融合及改进遗传算法的终端区进场协同排序[J]. 科学技术与工程, , ():

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  • 收稿日期:2021-11-18
  • 最后修改日期:2022-03-30
  • 录用日期:2022-04-30
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