电子与控制

独立离场模式下多跑道时空资源优化调度方法

  • 尹嘉男 ,
  • 胡明华 ,
  • 张洪海 ,
  • 马园园 ,
  • 吴凡
展开
  • 1. 南京航空航天大学 民航学院, 南京 211106;
    2. 国家空管飞行流量管理技术重点实验室, 南京 211106
尹嘉男 男, 博士研究生。主要研究方向: 机场规划、管理与评估, 空中交通流量管理,空中交通系统建模与仿真等。 Tel: 025-52112669 E-mail: yinjianan2121@126.com;胡明华 男, 教授, 博士生导师。主要研究方向: 国家空域系统规划、管理与评估, 飞行流量管理, 空中交通管理系统信息化与智能化等。 Tel: 025-52112079 E-mail: minghuahu@nuaa.edu.cn

收稿日期: 2014-06-23

  修回日期: 2014-09-11

  网络出版日期: 2014-09-26

基金资助

国家自然科学基金民航联合研究基金 (U1333202);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 (KYLX_0290)

Optimized method for multi-runway spatio-temporal resource scheduling in the mode of independent departures

  • YIN Jia'nan ,
  • HU Minghua ,
  • ZHANG Honghai ,
  • MA Yuanyuan ,
  • WU Fan
Expand
  • 1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
    2. National Key Laboratory of Air Traffic Flow Management, Nanjing 211106, China

Received date: 2014-06-23

  Revised date: 2014-09-11

  Online published: 2014-09-26

Supported by

Joint Funds of the National Natural Science Foundation and Civil Aviation Administration of China (U1333202);National Key Technology Research and Development Program of China (2011BAH24B09);Funding of Jiangsu Innovation Program for Graduate Education (KYLX_0290)

摘要

为有效缓解大流量、高密度机场日益严重的交通拥堵和航班延误现状,研究了多跑道离场航班优化调度问题。首先,从生产调度领域视角,将多跑道离场调度问题抽象为典型的车间作业调度NP-Hard组合优化问题;然后,面向航空运输各方利益需求,以航班延误、跑道容量和环境污染为优化目标,综合考虑航空器尾流影响、场面滑行和跑道穿越等各类限制因素,建立了独立离场模式下多跑道时空资源优化调度模型;最后,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(NSGA-II),寻求多跑道离场调度问题的Pareto最优解。仿真实验表明,模型可对独立离场航班进行优化配置,显著降低航班延误时间和航空发动机污染物排放量,并有效提升机场跑道容量。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51.2%和42.7%,所提方法可显著缓解大型繁忙机场离场航班起飞延误,有效提升航空运输服务品质。

本文引用格式

尹嘉男 , 胡明华 , 张洪海 , 马园园 , 吴凡 . 独立离场模式下多跑道时空资源优化调度方法[J]. 航空学报, 2015 , 36(5) : 1574 -1584 . DOI: 10.7527/S1000-6893.2014.0254

Abstract

In order to relieve the congestions and delays at busy airports with large flow and high density of air traffic, an optimized method for multi-runway spatio-temporal resource scheduling in the mode of independent departures is proposed in this work. Firstly, from the perspective of the field of production scheduling, multi-runway departure scheduling problem is regarded as an NP-Hard combinatorial optimization problem of typical job shop scheduling. Secondly, the optimization targets of flight delays, runway capacity and pollutant discharge amounts of aeroengine are established by deeply analyzing the needs of the stakeholders in air transportation industry, then an optimized model is established considering the restricts such as wake turbulence separation, surface taxiing separation and runway crossing separation. Finally, an elitist non-dominated sorting genetic algorithm (NSGA-Ⅱ) is designed combined with the multi-objective optimization theory and applied to solving the problem of multi-runway scheduling to search for Pareto optimal solutions. Simulation results show that the above model and algorithm can achieve optimized scheduling for aircraft in the mode of independent departures, effectively reduce the flight delays and pollutant discharge amounts of aeronautical engine and improve the runway capacity. Compared with the rand and alternate scheduling strategy of multi-runway, the optimized one brings about a striking effect which results in a 51.2% and 42.7% reduction in flight delays. The proposed method can significantly relieve the flight delays of departures at large busiest airport and effectively improve the service quality of air transportation.

参考文献

[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] Order 123. Management regulation for simultaneous operations on parallel instrument runways[S]. Beijing: Civil Aviation Administration of China, 2004: 1-17 (in Chinese). 第123号令. 平行跑道同时仪表运行管理规定[S]. 北京:中国民用航空局, 2004: 1-17.
[3] Heblij S J, Wijnen R A A. Development of a runway allocation optimisation model for airport strategic plan-ning[J]. Transportation Planning and Technology, 2008, 31(2): 201-214.
[4] Xu X H, Yao Y. Application of genetic algorithm to aircraft sequencing in terminal area[J]. Journal of Traffic and Transportation Engineering, 2004, 4(3): 121-126 (in Chinese). 徐肖豪, 姚源. 遗传算法在终端区飞机排序中的应用 [J]. 交通运输工程学报, 2004, 4(3): 121-126.
[5] Atkin J A D, Maere G D, Burke E K, et al. Addressing the pushback time allocation problem at Heathrow airport [J]. Transportation Science, 2013, 47(4): 584-602.
[6] Brinton C, Provan C, Lent S, et al. Collaborative departure queue management[C]//Proceedings of 9th USA/ Europe Air Traffic Management Research and Development Seminar. Brussels: Federal Aviation Administration/EUROCONTROL, 2011.
[7] Zhang X J, Guan X M, Sun D F, et al. The effect of queueing strategy on network traffic[J]. Communications in Theoretical Physics, 2013, 60(4): 496-502.
[8] Anagnostakis I, Clarke J P. Runway operations planning: a two-stage heuristic algorithm[C]//AIAA Aircraft, Technology, Integration and Operations Forum. Reston: AIAA, 2002.
[9] Anagnostakis I, Clarke J P. Runway operations planning: a two stage solution methodology[C]//Proceedings of the 36th Annual Hawaii International Conference on System Science. Hawaii: University of Hawaii at Manoa, 2003.
[10] Chick S, Sanchez P J, Ferrin D, et al. Runway schedule determination by simulation optimization[C]//Proceedings of the 2003 Winter Simulation Conference. Maryland: Simulation Society of INFORMS, 2003: 1670-1676.
[11] Yin J N, Hu M H, Peng Y, et al. Optimized method for multi-runway spatio-temporal resource scheduling in the mode of dependent approaches[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(11): 3064-3073 (in Chinese). 尹嘉男, 胡明华, 彭瑛, 等. 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.
[12] 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. Reston: AIAA, 2011.
[13] 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. Brussels: Federal Aviation Administration/EUROCONTROL, 1998.
[14] 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.
[15] European organization for safety air navigation. Phare advanced tools: departure manager final report, DOC 98-70-18[R]. Bruxelles: Programme for Harmonized Air Traffic Management Research, 1999.
[16] Australian Government. Runway allocator: interactive runway use analysis tool[R]. Canberra: Department of Transport and Regional Services, 2006.
[17] Srinivas N, Deb K. Multiobjective optimization using nondominated sorting in genetic algorithms[J]. Journal of Evolutionary Computation, 1994, 2(3): 221-248.
[18] 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.

文章导航

/