电子与控制

多跑道协同运行模式优化方法

  • 尹嘉男 ,
  • 胡明华 ,
  • 张洪海 ,
  • 马园园
展开
  • 1. 南京航空航天大学 民航学院, 江苏 南京 211106;
    2. 国家空管飞行流量管理技术重点实验室, 江苏 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,场面流量管理,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: 2013-05-21

  修回日期: 2013-07-23

  网络出版日期: 2013-08-08

基金资助

江苏省普通高校研究生科研创新计划项目(CXZZ12_0165);国家科技支撑计划(2011BAH24B09);国家自然科学基金(61104159);中央高校基本科研业务费专项资金

Optimization Approach for Collaborative Operating Modes of Multi-runway Systems

  • YIN Jia'nan ,
  • HU Minghua ,
  • ZHANG Honghai ,
  • MA Yuanyuan
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: 2013-05-21

  Revised date: 2013-07-23

  Online published: 2013-08-08

Supported by

Funding of Jiangsu Innovation Program for Graduate Education (CXZZ12_0165); National Key Technology Research and Development Program of China (2011BAH24B09); National Natural Science Foundation of China (61104159); Fundamental Research Funds for the Central Universities

摘要

繁忙机场飞行区运行能力低下导致空中交通拥堵及航班延误现象频发,机场系统亟需扩容增效与排堵保畅。为有效平衡机场容流供需,研究了多跑道协同运行模式优化方法。综合考虑机场布局、交通流特性、气象条件等因素,提出了多跑道协同运行模式分类方法;基于跑道容量包络线理论,通过引入容量损失系数客观反映模式切换特点,建立了多跑道协同运行模式优化模型;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(NSGA),对模型进行了准确求解。仿真实验表明,模型可对多跑道协同运行模式进行优化配置,有效实现机场容量与流量之间的均衡。与单一固定模式相比,多元组合模式优化效果显著,其中航班延误成本减少了38.1%,航班调整数量减少了46.4%,所提方法可显著提升多跑道协同运行能力,有效提高航班正常性。

本文引用格式

尹嘉男 , 胡明华 , 张洪海 , 马园园 . 多跑道协同运行模式优化方法[J]. 航空学报, 2014 , 35(3) : 795 -806 . DOI: 10.7527/S1000-6893.2013.0350

Abstract

Low service ability of an airfield area causes frequent air traffic congestion and flight delays at busy airports. The airport system calls for capacity and efficiency improvements urgently to relieve the current congested situation. In this work, an optimization approach for the collaborative operating modes of multi-runway systems is proposed to balance the demand and capacity. Runway operating modes are classified in detail taking into consideration the airport layout, air traffic characteristics, weather conditions and other factors comprehensively. Based on the theory of runway capacity envelope, a corresponding optimization model is established by introducing the capacity loss coefficient which objectively reflects the mode switching characteristics. Then an elitist non-dominated sorting genetic algorithm is designed combined with the multi-objective optimization theory, and applied to solve the problem of multi-runway operating mode configuration. Simulation results show that the above model and algorithm can achieve optimized multi-runway configuration and improve demand-capacity balancing. Compared with the single runway mode, the combined runway modes bring about a striking optimization effect which results in a 38.1% reduction in the cost of flight delays and a 46.4% decrease in the quantity of adjusted flights. The approach provided can significantly enhance collaborative operating efficiency of a multi-runway system, and effectively improve air traffic punctuality.

参考文献

[1] Beasley J E, Krishnamoorthy M, Sharaiha Y M, et al. Scheduling aircaft landings-the static case[J]. Transportation Science, 2000, 34(2): 180-197.

[2] Romano E, Santillo L C, Zoppoli P. A static algorithm to solve the air traffic sequencing problem[J]. WSEAS Transactions on Systems, 2008, 7(6): 682-695.

[3] Bojanowski L, Harikiopoulo D, Neogi N. Multi-runway aircraft sequencing at congested airports[C]//Proceedings of American Control Conference, 2011: 2752-2758.

[4] 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, 2011.

[5] Czerny A I. Airport congestion management under uncertainty[J]. Transportation Research Part B: Methodological, 2010, 44 (3): 371-380.

[6] Schank J L. Solving airside airport congestion: why peak runway pricing is not working [J]. Journal of Air Transport Management, 2005, 11 (6): 417-425.

[7] Yin J N, Hu M H, Zhao Z. Simulation model and algorithm of multi-runway airport gate assignment[J]. Journal of Traffic and Transportation Engineering, 2010, 10(5): 71-76. (in Chinese) 尹嘉男, 胡明华, 赵征. 多跑道机场停机位分配仿真模型及算法[J]. 交通运输工程学报, 2010, 10(5): 71-76.

[8] Australian Government. Runway allocator: interactive runway use analysis tool[R]. Canberra: Department of Transport and Regional Services, 2006.

[9] Gluchshenko O. Dynamic usage of capacity for arrivals and departures in queue minimization[C]//Proceedings of IEEE International Conference on Control Applications, 2011: 139-146.

[10] Solak S, Clarke J P B, Johnson E L. Airport terminal capacity planning [J]. Transportation Research Part B: Methodological, 2009, 43 (6): 659-676.

[11] Bertsimas D, Frankovich M, Odoni A. Optimal selection of airport runway configurations [J]. Operations Research, 2011, 59 (6): 1407-1419.

[12] Gilbo E P, Howard K W. Collaborative optimization of airport arrival and departure traffic flow management strategies for CDM[C]//Proceedings of 3rd USA/Europe Air Traffic Management Research and Development Seminar, 2000: 13-16.

[13] Gluchshenko O. Optimization of runway capacity utilization in the case of general pareto curve[C]//Proceedings of 28th International Congress of the Aeronautical Sciences. Brisbane, 2012: 4183-4199.

[14] Barrer J N, Kuzminski P, Swedish W J. Analyzing the runway capacity of complex airports [C]//Proceedings of 5th AIAA Aviation Technology, Integration and Operations Conference, 2005.

[15] Gilbo E P. Airport capacity: representation, estimation, optimization[J]. IEEE Transactions on Control Systems Technology, 1993, 1 (3): 144-154.

[16] Federal Aviation Administration. Airport capacity and delay, No.150/5060-5[R]. Washington, DC: FAA, 1983.

[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.

[19] Srinivas M, Patnaik L M. Adaptive probabilities of crossover and mutation in genetic algorithms [J]. IEEE Transactions on Systems, Man and Cybernetics, 1994, 24(4): 656-667.

[20] Bingul Z. Adaptive genetic algorithms applied to dynamic multiobjective problems [J]. Applied Soft Computing, 2007, 7 (3): 791-799.

文章导航

/