相关进近模式下多跑道时空资源优化调度方法
收稿日期: 2014-01-01
修回日期: 2014-02-24
网络出版日期: 2014-03-17
基金资助
国家自然科学基金(61104159, 71301074);国家科技支撑计划 (2011BAH24B09);江苏省普通高校研究生科研创新计划 (CXZZ12_0165);中央高校基本科研业务费
Optimized Method for Multi-runway Spatio-temporal Resource Scheduling in Mode of Dependent Approaches
Received date: 2014-01-01
Revised date: 2014-02-24
Online published: 2014-03-17
Supported by
National Natural Science Foundation of China (61104159, 71301074); National Key Technology Support Program of China (2011BAH24B09); Funding of Jiangsu Innovation Program for Graduate Education (CXZZ12_0165); Fundamental Research Funds for the Central Universities
跑道作为机场飞行区与终端区的衔接部分,因容流失衡频繁引发空中交通拥堵及航班延误等问题.为提高多跑道系统运行能力,针对相关进近跑道运行模式,研究了多跑道时空资源优化调度方法.通过深入剖析多跑道时空运行特性,综合考虑移交间隔、尾流间隔、纵向间隔和相关斜距等约束限制,科学权衡安全、经济和环境等各方利益需求,建立了相关进近模式下多跑道时空资源优化调度模型.结合多目标优化及遗传算法基本理论,设计了带精英策略的非支配排序遗传算法,对模型进行了准确求解.仿真实验表明,模型可对相关进近航班进行优化配置,有效降低航班延误时间和航空发动机污染物排放量.与随机和交替调度策略相比,优化调度策略执行效果显著,其中航班延误时间分别减少了39.3%和32.6%,所提方法可显著缓解大型繁忙机场航班延误情况,有效提高航班正常率,并完全适用于独立进近模式.
尹嘉男 , 胡明华 , 彭瑛 , 唐勇 , 马园园 . 相关进近模式下多跑道时空资源优化调度方法[J]. 航空学报, 2014 , 35(11) : 3064 -3073 . DOI: 10.7527/S1000-6893.2014.0001
As the convergence part of the airfield and terminal area at an airport system, the runway causes frequent air traffic congestions and flight delays due to the unbalanced demand and capacity. In this work, an optimized method for multi-runway spatio-temporal resource scheduling in the mode of dependent approaches is proposed to improve the service ability of multi-runway systems. By analyzing in depth the spatio-temporal characteristics of multi-runway systems and taking into considerations the control handoff separation, wake turbulence separation, longitudinal separation, and slope separation, an optimized model is established to balance different factors of interest such as safety, economy and environment. Then an elitist non-dominated genetic sorting algorithm is designed in combination with the multi-objective optimization theory, and applied to solve the problem of multi-runway scheduling. Simulation results show that the above model and algorithm can achieve optimized scheduling for aircraft in dependent approaches, and it can effectively reduce flight delays as well as pollutant discharges of aeronautical engines. Compared with the random and alternate scheduling strategy of multi-runway, the optimized model brings about a striking effect of 39.3% and 32.6% reduction in flight delays. The method which is entirely applicable to the mode of independent approaches can significantly relieve the flight delays at large busy airports, and effectively improve the flight punctuality rate.
[1] Yin J N, Hu M H, Zhang H H, et al. Optimization approach for collaborative operating modes of multi-runway systems[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(3): 795-806. (in Chinese) 尹嘉男, 胡明华, 张洪海, 等. 多跑道协同运行模式优化方法[J]. 航空学报, 2014, 35(3): 795-806.
[2] Heblij S J, Wijnen R A A. Development of a runway allocation optimisation model for airport strategic planning[J]. Transportation Planning and Technology, 2008, 31(2): 201-214.
[3] Bauerle N, Engelhardt-Funke O, Kolonko M. On the waiting time of arriving aircrafts and the capacity of airports with one or two runways[J]. European Journal of Operational Research, 2007, 177(2): 1180-1196.
[4] Bauerle N, Engelhardt-Funke O, Kolonko M. Routing of airplanes to two runways: monotonicity of optimal controls[J]. Probability in the Engineering and Informational Sciences, 2004, 18(4): 533-560.
[5] Chick S, Sanchez P J, Ferrin D, et al. Runway schedule determination by simulation optimization[C]//Proceedings of the 2003 Winter Simulation Conference, 2003: 1670-1676.
[6] Martyna J. Runway scheduling with holding pattern and service priority[J]. Automatics, 2012, 16(2): 137-144.
[7] Kuiper B R, Visser H G, Heblij S. Efficient use of the noise budget at schiphol airport through minimax optimization of runway allocations[C]//Proceedings of the 11th AIAA Aviation Technology, Integration, and Operations Conference, 2011:1-15.
[8] Saffarzadeh M, Abadi I N K, Kordani A A, et al. A new approach in airport capacity enhancement based on integrated runway assignment and operations planning model[J]. Journal of Applied Sciences, 2008, 8(22): 4040-4050.
[9] Yang Q H, You Z S, Feng Z L, et al. Scheduling arrival aircrafts on multiple runways based on an improved genetic algorithm[J]. Journal of Sichuan University: Engineering Science Edition, 2006, 38(2): 141-145. (in Chinese) 杨秋辉, 游志胜, 冯子亮, 等. 一种改进的基于遗传算法的多跑道到达飞机调度[J]. 四川大学学报工程科学版, 2006, 38(2): 141-145.
[10] Zhang H H, Hu M H. Multi-runway collaborative scheduling optimization of aircraft landing[J]. Journal of Traffic and Transportation Engineering, 2009, 9(3): 86-91. (in Chinese) 张洪海, 胡明华. 多跑道降落飞机协同调度优化[J]. 交通运输工程学报, 2009, 9(3): 86-91.
[11] Gupta G, Malik W, Jung Y C. Effect of uncertainty on deterministic runway scheduling[C]//Proceedings of the 11th AIAA Aviation Technology, Integration, and Operations Conference, 2011: 1-16.
[12] Smith C, Piggott A, Morris C, et al. Final approach spacing tool[C]//Proceedings of 2nd USA/Europe Air Traffic Management Research and Development Seminar, 1998: 1-11.
[13] European Organization for Safety Air Navigation. Phare advanced tools: arrival manager final report, DOC 98-70-18[R]. Bruxelles: Programme for Harmonized Air Traffic Management Research, 1998.
[14] Australian Government. Runway allocator: interactive runway use analysis tool[R]. Canberra: Department of Transport and Regional Services, 2006.
[15] Srinivas N, Deb K. Multiobjective optimization using nondominated sorting in genetic algorithms[J]. Journal of Evolutionary Computation, 1994, 2(3): 221-248.
[16] Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
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