Volume 41 Issue 5
Oct.  2023
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LU Tingting, LIU Jimin, QU Chenrui, ZHANG Zhaoning. A Cooperative Recovery Strategy for Massive Flight Delays Based on Satisficing Game Theory[J]. Journal of Transport Information and Safety, 2023, 41(5): 95-106. doi: 10.3963/j.jssn.1674-4861.2023.05.010
Citation: LU Tingting, LIU Jimin, QU Chenrui, ZHANG Zhaoning. A Cooperative Recovery Strategy for Massive Flight Delays Based on Satisficing Game Theory[J]. Journal of Transport Information and Safety, 2023, 41(5): 95-106. doi: 10.3963/j.jssn.1674-4861.2023.05.010

A Cooperative Recovery Strategy for Massive Flight Delays Based on Satisficing Game Theory

doi: 10.3963/j.jssn.1674-4861.2023.05.010
  • Received Date: 2022-01-09
    Available Online: 2024-01-18
  • Severe weather conditions, intervention from military activities, and other unforeseen hazards frequently lead to the massive flight delays in the aviation sector. This will bring significant economic losses for airports and airlines and may even result in problems such as incidents due to passenger crowding at airports. Recovery of massive flight delays involves the operation and related interests of multiple stakeholders, such as air traffic control, airports and airlines. Therefore, it is necessary to study a cooperative recovery strategy based on the satisfaction of the above parties to guide the optimal and rapid recovery of massive flight delays in practical airport operation. Applying the satisficing game theory method considers various costs, including the impact of the delayed flights on ramp control, congestion within control sectors, the entire air traffic network, and the economic losses of airlines. This study also analyzes the factors influencing the recovery operations and decisions for delayed flights by proposing a model that maximizes air flow while ensuring the recovery of flights without further delay. Additionally, a collaborative recovery strategy model for air traffic control, airports, and airlines under massive flight delays based on the principles of satisficing game theory is developed. The model considers the principles of air traffic control release flow, airport capacity, and the recovery of delayed flights by airlines. An illustrative case study is conducted for the recovery of 50 delayed flights that were scheduled to depart from Beijing Capital International Airport from 07:00 to 12:30. The findings show that, the proposed model and methodology facilitate the recovery of 32 flights during 12:30 to 16:30 time frame, showcasing a 10.34% increase compared to the actual recovery of 29 flights. Moreover, the estimated order of flight recovery and the time window for each flight's recovery reduce the economic losses incurred by airlines by approximately 3 million Chinese Yuan and save approximately 19 hours in time costs. The strategy also effectively reduces the flight adjustment volume, significantly mitigates the flight delay losses, and enhances the overall benefits of flight recovery, thus validating the effectiveness of the recovery strategy model.

     

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  • [1]
    TEODOROVIC D, GUBERINIC S. Optimal dispatching strategy on an airline network after a schedule perturbation[J]. European Journal of Operational Research, 1984, 15 (2): 178-182. doi: 10.1016/0377-2217(84)90207-8
    [2]
    CAO J, KANAFANI A. Real-time decision support for integration of airline flight cancellations and delays, part I: mathematical formulations[J]. Transportation Planning and Technology, 1997, 20(3): 183-199. doi: 10.1080/03081069708717588
    [3]
    ARIAS P, MOTA M, GUIMARANS D, et al. A methodology combining optimization and simulation for real applications of the stochastic aircraft recovery problem[C]. 8th EUROSIM Congress on Modelling and Simulation, Cardiff, UK: 2013, IEEE, 2013.
    [4]
    BRATU S, BARNHART C. Flight operations recovery: new approaches considering passenger recovery[J]. Springer Science Business Media, 2006, 13(9): 279-298.
    [5]
    PEI S, HE Y, FAN Z, et al. Decision support system for the irregular flight recovery problem[J]. Research in Transportation Business and Management, 2020, 38(2): 100501.
    [6]
    詹晨旭, 乐美龙. 非正常航班管理中的飞机恢复问题研究[J]. 中国民航大学学报, 2012, 30(2): 43-47. doi: 10.3969/j.issn.1001-5590.2012.02.011

    ZHAN C X, LE M L. Study on aircraft recovery problem under airline's irregular flight management[J]. Journal of Civil Aviation University of China, 2012, 30(2): 43-47. (in Chinese) doi: 10.3969/j.issn.1001-5590.2012.02.011
    [7]
    曲倩倩. 混合遗传算法求解航班延误恢复调度[J]. 科技创新与应用, 2013(16): 28-29.

    QU Q Q. Hybrid genetic algorithm for recovery scheduling of flight delays[J]. Technology Innovation and Application, 2013 (16): 28-29. (in Chinese)
    [8]
    陆荣琴, 赵小梅, 毕军, 等. 基于航班综合影响因素的延误航班重排问题的整数规划模型[J]. 交通运输研究, 2017, 3 (3): 36-42.

    LU R Q, ZHAO X M, BI J, et al. An integral programming model to reschedule delayed flights based on comprehensive factors[J]. Transport Research, 2017, 3(3): 36-42. (in Chinese)
    [9]
    王小萌, 牟奇锋, 周瑾. 航班延误成本量化方法研究[J]. 科技和产业, 2023, 23(18): 151-155.

    WANG X M, MOU Q F, ZHOU J. Research on quantitative methods for flight delay costs[J]. Science Technology and Industry, 2023, 23(18): 151-155. (in Chinese)
    [10]
    任杰, 王莉莉. 基于最小延误时间和经济成本的空中高速路匝口排序研究[J]. 数学的实践与认识, 2023, 53(5): 22-31.

    REN J, WANG L L. Study on aircraft scheduling for highway-in-the-sky ramp based on the minimum delay cost model[J]. Mathematics in Practice and Theory, 2023, 53(5): 22-31. (in Chinese)
    [11]
    LEE J, MARLA L, JACQUILLAT A. Dynamic disruption management in airline networks under airport operating uncertainty[J]. Transportation Science, 2020, 54 (4): 973-997. doi: 10.1287/trsc.2020.0983
    [12]
    VINK J, SANTOS B F, VERHAGEN W, et al. Dynamic aircraft recovery problem - An operational decision support framework[J]. Computers & Operations Research, 2020, 117: 1-15.
    [13]
    徐海文, 汪腾. 考虑航路网络结构的离场航班延误预测模型[J]. 科学技术与工程, 2023, 23(11): 4734-4744.

    XU H W, WANG T. Departure flight delay predication model considering air route network structure[J]. Science Technology and Engineering, 2023, 23(11): 4734-4744. (in Chinese)
    [14]
    SHAO Q, SHAO M X, BIN Y P, et al. Flight recovery method of regional multiairport based on risk control model[J]. Mathematical Problems in Engineering, 2020(7): 1-18.
    [15]
    何坚, 果红艳, 姚远, 等. 基于有效中转时间预测的不正常航班恢复技术[J]. 北京航空航天大学学报, 2022, 48(3): 384-393.

    HE J, GUO H Y, YAO Y, et al. Irregular flight recovery technique based on accurate transit time prediction[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48 (3): 384-393. (in Chinese)
    [16]
    严宇, 熊静, 陈聪聪, 等. 基于跑道容量的航班恢复优化调度[J]. 物流科技, 2022, 45(2): 99-102, 112.

    YAN Y, XIONG J, CHEN C C, et al. Optimal scheduling of flight recovery based on runway capacity[J]. Logistics Sci-Tech, 2022, 45(2): 99-102, 112. (in Chinese)
    [17]
    王楠, 戴福青, 齐雁楠. 基于跑道容量的航班恢复优化模型[J]. 科学技术与工程, 2020, 20(15): 6279-6285.

    WANG N, DAI F Q, QI Y N. Flight recovery optimization model based on runway capacity[J]. Science Technology and Engineering, 2020, 20(15): 6279-6285. (in Chinese)
    [18]
    张静, 徐明华, 曹伟建, 等. 不正常航班恢复模型和算法研究[J]. 数学的实践与认识, 2018, 48(15): 145-152.

    ZHANG J, XU M H, CAO W J, et al. Research on modeling and algorithm for irregular flight recovery[J]. Mathematics in Practice and Theory, 2018, 48(15): 145-152. (in Chinese)
    [19]
    杨旻昊. 航班次衍生延误传播机理研究[D]. 南京: 航空航天大学, 2020.

    YANG M H. Research on the mechanism of flight derivative delay propagation[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2020. (in Chinese)
    [20]
    王晶华. 机场大面积航班延误传播模型及预测研究[D]. 中国民航大学, 2019.

    WANG J H. Research on propagation model and prediction of airport large-scale flight delay[D]. Tianjin: Civil Aviation University of China, 2019. (in Chinese)
    [21]
    武喜萍, 杨红雨, 韩松臣. 基于复杂网络的空中交通特征与延误传播分析[J]. 航空学报, 2017, 38(增刊1): 113-119.

    WU X P, YANG H Y, HAN S C. Analysis of properties and delay propagation of air traffic based on complex network[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38 (S1): 113-119. (in Chinese)
    [22]
    闵嘉诚. 基于Copula函数和贝叶斯网络的航班延误波及效应研究[D]. 苏州: 苏州大学, 2021.

    MIN J C. A study on flight delay propagation based on Copula function and Bayesian network[D]. Suzhou: Soochow University, 2021. (in Chinese)
    [23]
    王兴隆, 许晏丰, 纪君柔. 基于VMD-MD-Clustering方法的航班延误等级分类[J]. 交通信息与安全, 2022, 40(3): 171-178. doi: 10.3963/j.jssn.1674-4861.2022.03.018

    WANG X L, XU Y F, JI J R. Classification of the level of flight delay based on aVMD-MD-Clustering method[J]. Journal of Transport Information and Safety, 2022, 40(3): 171-178. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2022.03.018
    [24]
    管祥民, 吕人. 基于满意博弈论的复杂低空飞行冲突解脱方法[J]. 航空学报, 2017, 38(增刊1): 120-128.

    GUAN X M, LYU R. Aircraft conflict resolution method based on satisfying game theory[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(S1): 120-128. (in Chinese)
    [25]
    史妙恬. 航空运输网络中不确定性因素对航班延误波及的影响研究[D]. 南京: 南京航空航天大学, 2017.

    SHI M T. Research on the flight delay propagation in aviation network under uncertainty factor[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2017. (in Chinese)
    [26]
    张洪海, 胡明华. CDM GDP飞机着陆时隙多目标优化分配[J]. 系统管理学报, 2009, 18(3): 302-308.

    ZHANG H H, HUM H. Multi-objection optimization allocation of aircraft landing slot in CDM GDP[J]. Journal of Systems & Management, 2009, 18(3): 302-308. (in Chinese)
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