恶劣天气下多航空器改航路径的仿真优化算法

朱承元; 晏楠欣

doi:10.3963/j.jssn.1674-4861.2021.02.014

恶劣天气下多航空器改航路径的仿真优化算法

doi: 10.3963/j.jssn.1674-4861.2021.02.014

朱承元^,,
晏楠欣

中国民航大学空中交通管理学院天津 300300

基金项目:

国家自然科学基金青年科学基金项目 U1833103

详细信息

通讯作者:
朱承元(1965—)，博士，副教授.研究方向：空域规划与仿真.E-mail: cyzhu@cauc.edu.cn

中图分类号: X951
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出版历程
- 收稿日期: 2021-01-24

A Simulation Optimization Algorithm for Multi-aircraft Rerouting in Severe Weather

ZHU Chengyuan^,,
YAN Nanxin

College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China

摘要

摘要: 针对恶劣天气下区域管制区内，多航空器改航路径规划中缺乏降低管制员工作总负荷的考虑。以贵阳区域管制区为例，研究了恶劣天气下多航空器改航路径的仿真优化算法。采用灰色模型预测飞行受限区的动态影响范围；利用几何算法预先规划可供选择的改航路径；改进离散粒子群优化算法的运算规则；以整个区域管制区内改航总路径最短和管制员工作总负荷最低为目标，结合预测的飞行受限区、预先规划的改航路径、改进离散粒子群优化算法和全空域与机场模型实现恶劣天气下多航空器改航路径的仿真优化算法。结果表明，该仿真优化算法经过多次迭代，获得了改航优化方案；与采用传统粒子群算法的仿真优化算法相比，管制员工作总负荷下降了7.52%，改航总路径距离减少了4.48%；与采用多目标粒子群算法和非支配排序遗传算法-II的改航路径算法相比，其改航路径距离略长，但考虑了管制员工作负荷的影响。该仿真优化算法能在减少改航路径距离的同时有效降低管制员工作负荷，对实际改航规划具有借鉴意义。
- 空中交通管理 /
- 改航路径 /
- 仿真优化算法 /
- 离散粒子群优化算法 /
- 恶劣天气 /
- 管制员工作负荷
Abstract: A simulation optimization algorithm for multiple aircraft diversion routes under severe weather is studied to address the lack of consideration for reducing the total controller workload in the planning of multiple aircraft re-routes in the regional control area(RCA)during severe weather. Guiyang RCA is taken as a case study. A gray model is used to predict the dynamic impact range of the flight forbidden area(FFA), and a geometric algorithm is used to plan the alternative diversion routes, with the operation rules of the discrete particle swarm optimization algorithm (DPSO)improved. The simulation optimization algorithm of multiple aircraft diversion routes in severe weather is implemented by combining the predicted FFA, the pre-planned diversion routes, the improved DPSO algorithm, and the total airspace and airport modeler(TAAM)to minimize the total diversion routes and the total controller workload in the whole area. The results show that the simulation optimization algorithm, after several iterations, can obtain a diverting optimization scheme. The total controller workload decreases by 7.52%, and the total distance of diverting routes decreases by 4.48%, compared with the simulation optimization algorithm using the traditional particle swarm optimization algorithm(PSO). It has a slightly longer distance of diverting routes compared with the rerouting path algorithm using the multi-objective particle swarm algorithm(MOPSO)and the non-dominated sorted genetic algorithm-II(NSGA-II). However, the influences of controller workload should be considered. The simulation optimization algorithm can reduce the effective controller workload and the distance of diverting routes, which is useful for the planning of actually diverting routes.
- air traffic management /
- diverting routes /
- simulation optimization algorithm /
- discrete particle-swarm optimization algorithm /
- severe weather /
- controller workload

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图 1 划设初始飞行受限区

Figure 1. Designation of the initial flight-forbidden area

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图 2 仿真优化算法的流程图

Figure 2. Flow of the simulated optimization algorithm

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图 3 改航点的确定

Figure 3. Determination of diverting points

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图 4 多航空器改航路径示意图

Figure 4. Diverting routes of multi aircrafts

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图 5 恶劣天气对应的飞行受限区图示

Figure 5. Flight-forbidden area corresponding to severe weather

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图 6 改航环境和改航路径的图示

Figure 6. Diagrammatic representation of the redirected environment and diverting routes

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图 7 管制员工作小时负荷图示

Figure 7. Graphical representation of workhour load of the controller

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图 8 改航环境下算法适应度的变化

Figure 8. Variation of algorithm's adaptation in diversion

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图 9 MOPSO算法和NSGA-II算法的改航路径结果

Figure 9. Results of the MOPSO algorithm and the NSGA-II algorithm for diversion

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表 1 改航数据汇总

Table 1. Summary of diverting data

	Origin	PSO	DPSO
管制员工作总负荷/ (当量架次）	216.21	256.85	237.54
改航总路径距离/(n mile)	3 572.10	4 437.30	4 238.70

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表 2 CDC8823航班的改航数据

Table 2. Date of the diversion of Flight CDC8823

位置	时间	距离	位置	时间	距离	位置	时间	距离
ZSHC	16：48：00	0	ZSHC	16：48：00	0	ZSHC	16：48：00	0
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
P159	18：04：18	559.6	P159	18：04：18	559.6	P159	18：04：18	559.6
ZHJ	18：11：55	599	ZHJ	18：11：55	599	ZHJ	18：11：55	599
P293	18：14：45	619.8	P293	18：14：44	619.7	P293	18：14：45	619.8
XONID	18：21：30	669.4	B3	18：18：23	646.5	B1	18：18：47	649.4
UBDID	18：28：08	718.1	B1	18：18：58	650.8	XONID	18：21：04	665.2
MASRO	18：31：29	742.6	B2	18：19：25	654.1	UGUGU	18：24：59	684.9
⋮	⋮	⋮	XONID	18：21：03	666.1	A1	18：29：08	715.4
ZUBJ	18：54：34	861.8	UGUGU	18：25：03	685.4	UBDID	18：32：07	732.3
			A3	18：29：12	715.9	MASRO	18：35：57	768.4
			A1	18：30：08	722.7	⋮	⋮	⋮
			A2	18：30：24	724.7	ZUBJ	18：59：01	894.5
			UBDID	18：32：41	736.5
			MASRO	18：36：30	772.5
			⋮	⋮	⋮
			ZUBJ	18：59：37	898.7

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