IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i11p2577-d1163662.html
   My bibliography  Save this article

A Data-Driven Heuristic Method for Irregular Flight Recovery

Author

Listed:
  • Nianyi Wang

    (Laboratory of Mathematics and Complex Systems, Ministry of Education, School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China)

  • Huiling Wang

    (Laboratory of Mathematics and Complex Systems, Ministry of Education, School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China)

  • Shan Pei

    (HSBC Business School, Peking University, Shenzhen 518055, China)

  • Boyu Zhang

    (Laboratory of Mathematics and Complex Systems, Ministry of Education, School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China)

Abstract

In this study, we develop a data-driven heuristic method to solve the irregular flight recovery problem. Based on operational data from China South Airlines, Beijing, China, we evaluate the importance of a flight in the flight network and the influence of a delay on a flight and its subsequent flights. Then, we classify historical states into three scenarios according to their delay reasons and investigate the recovery patterns for each scenario. Inspired by the results of the data analysis, we develop a heuristic algorithm that imitates dispatcher actions. The algorithm is based on two basic operations: swapping the tail numbers of two flights and resetting their flight departure times. The algorithm can provide multiple recovery plans in real time for different scenarios, and we continue to refine and validate the algorithm for more robust and general solutions through a cost analysis. Finally, we test the efficiency and effectiveness of the recovery method based on the flight schedule, with real and simulated delays, and compare it with two other methods and the recovery actions of dispatchers.

Suggested Citation

  • Nianyi Wang & Huiling Wang & Shan Pei & Boyu Zhang, 2023. "A Data-Driven Heuristic Method for Irregular Flight Recovery," Mathematics, MDPI, vol. 11(11), pages 1-22, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:11:p:2577-:d:1163662
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/11/2577/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/11/2577/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Benjamin G. Thengvall & Jonathan F. Bard & Gang Yu, 2003. "A Bundle Algorithm Approach for the Aircraft Schedule Recovery Problem During Hub Closures," Transportation Science, INFORMS, vol. 37(4), pages 392-407, November.
    2. Hu, Yuzhen & Song, Yan & Zhao, Kang & Xu, Baoguang, 2016. "Integrated recovery of aircraft and passengers after airline operation disruption based on a GRASP algorithm," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 87(C), pages 97-112.
    3. Christopher Bayliss & Geert Maere & Jason A. D. Atkin & Marc Paelinck, 2017. "A simulation scenario based mixed integer programming approach to airline reserve crew scheduling under uncertainty," Annals of Operations Research, Springer, vol. 252(2), pages 335-363, May.
    4. Ahmed Khassiba & Fabian Bastin & Sonia Cafieri & Bernard Gendron & Marcel Mongeau, 2020. "Two-Stage Stochastic Mixed-Integer Programming with Chance Constraints for Extended Aircraft Arrival Management," Transportation Science, INFORMS, vol. 54(4), pages 897-919, July.
    5. Kohl, Niklas & Larsen, Allan & Larsen, Jesper & Ross, Alex & Tiourine, Sergey, 2007. "Airline disruption management—Perspectives, experiences and outlook," Journal of Air Transport Management, Elsevier, vol. 13(3), pages 149-162.
    6. T. Andersson * & P. Värbrand, 2004. "The flight perturbation problem," Transportation Planning and Technology, Taylor & Francis Journals, vol. 27(2), pages 91-117, March.
    7. Jean-François Cordeau & Goran Stojković & François Soumis & Jacques Desrosiers, 2001. "Benders Decomposition for Simultaneous Aircraft Routing and Crew Scheduling," Transportation Science, INFORMS, vol. 35(4), pages 375-388, November.
    8. Sinclair, Karine & Cordeau, Jean-François & Laporte, Gilbert, 2014. "Improvements to a large neighborhood search heuristic for an integrated aircraft and passenger recovery problem," European Journal of Operational Research, Elsevier, vol. 233(1), pages 234-245.
    9. Shan Lan & John-Paul Clarke & Cynthia Barnhart, 2006. "Planning for Robust Airline Operations: Optimizing Aircraft Routings and Flight Departure Times to Minimize Passenger Disruptions," Transportation Science, INFORMS, vol. 40(1), pages 15-28, February.
    10. Stephen J. Maher, 2016. "Solving the Integrated Airline Recovery Problem Using Column-and-Row Generation," Transportation Science, INFORMS, vol. 50(1), pages 216-239, February.
    11. Mirela Stojković & François Soumis & Jacques Desrosiers, 1998. "The Operational Airline Crew Scheduling Problem," Transportation Science, INFORMS, vol. 32(3), pages 232-245, August.
    12. Jon D. Petersen & Gustaf Sölveling & John-Paul Clarke & Ellis L. Johnson & Sergey Shebalov, 2012. "An Optimization Approach to Airline Integrated Recovery," Transportation Science, INFORMS, vol. 46(4), pages 482-500, November.
    13. N Jozefowiez & C Mancel & F Mora-Camino, 2013. "A heuristic approach based on shortest path problems for integrated flight, aircraft, and passenger rescheduling under disruptions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(3), pages 384-395, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jane Lee & Lavanya Marla & Alexandre Jacquillat, 2020. "Dynamic Disruption Management in Airline Networks Under Airport Operating Uncertainty," Transportation Science, INFORMS, vol. 54(4), pages 973-997, July.
    2. Naz Yeti̇moğlu, Yücel & Selim Aktürk, M., 2021. "Aircraft and passenger recovery during an aircraft’s unexpected unavailability," Journal of Air Transport Management, Elsevier, vol. 91(C).
    3. Ding, Yida & Wandelt, Sebastian & Wu, Guohua & Xu, Yifan & Sun, Xiaoqian, 2023. "Towards efficient airline disruption recovery with reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    4. Hanif D. Sherali & Ki-Hwan Bae & Mohamed Haouari, 2013. "An Integrated Approach for Airline Flight Selection and Timing, Fleet Assignment, and Aircraft Routing," Transportation Science, INFORMS, vol. 47(4), pages 455-476, November.
    5. Stephen J. Maher, 2016. "Solving the Integrated Airline Recovery Problem Using Column-and-Row Generation," Transportation Science, INFORMS, vol. 50(1), pages 216-239, February.
    6. Pedro Jose Gudiel Pineda & Chao-Che Hsu & James J. H. Liou & Huai-Wei Lo, 2018. "A Hybrid Model for Aircraft Type Determination Following Flight Cancellation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(04), pages 1147-1172, July.
    7. Jon D. Petersen & Gustaf Sölveling & John-Paul Clarke & Ellis L. Johnson & Sergey Shebalov, 2012. "An Optimization Approach to Airline Integrated Recovery," Transportation Science, INFORMS, vol. 46(4), pages 482-500, November.
    8. Gary Froyland & Stephen J. Maher & Cheng-Lung Wu, 2014. "The Recoverable Robust Tail Assignment Problem," Transportation Science, INFORMS, vol. 48(3), pages 351-372, August.
    9. Evelien van der Hurk & Leo Kroon & Gábor Maróti, 2018. "Passenger Advice and Rolling Stock Rescheduling Under Uncertainty for Disruption Management," Service Science, INFORMS, vol. 52(6), pages 1391-1411, December.
    10. Uğur Arıkan & Sinan Gürel & M. Selim Aktürk, 2016. "Integrated aircraft and passenger recovery with cruise time controllability," Annals of Operations Research, Springer, vol. 236(2), pages 295-317, January.
    11. Uğur Arıkan & Sinan Gürel & M. Aktürk, 2016. "Integrated aircraft and passenger recovery with cruise time controllability," Annals of Operations Research, Springer, vol. 236(2), pages 295-317, January.
    12. Huang, Zhouchun & Luo, Xiaodong & Jin, Xianfei & Karichery, Sureshan, 2022. "An iterative cost-driven copy generation approach for aircraft recovery problem," European Journal of Operational Research, Elsevier, vol. 301(1), pages 334-348.
    13. Uğur Arıkan & Sinan Gürel & M. Selim Aktürk, 2017. "Flight Network-Based Approach for Integrated Airline Recovery with Cruise Speed Control," Transportation Science, INFORMS, vol. 51(4), pages 1259-1287, November.
    14. Kenan, Nabil & Jebali, Aida & Diabat, Ali, 2018. "The integrated aircraft routing problem with optional flights and delay considerations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 355-375.
    15. Delgado, Felipe & Mora, Julio, 2021. "A matheuristic approach to the air-cargo recovery problem under demand disruption," Journal of Air Transport Management, Elsevier, vol. 90(C).
    16. Derui Wang & Yanfeng Wu & Jian-Qiang Hu & Miaomiao Liu & Peiwen Yu & Cheng Zhang & Yan Wu, 2019. "Flight Schedule Recovery: A Simulation-Based Approach," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(06), pages 1-19, December.
    17. Vieira, Thiago & De La Vega, Jonathan & Tavares, Roberto & Munari, Pedro & Morabito, Reinaldo & Bastos, Yan & Ribas, Paulo César, 2021. "Exact and heuristic approaches to reschedule helicopter flights for personnel transportation in the oil industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).
    18. Kim, Myeonghyeon & Choi, Yuri & Song, Ki Han, 2019. "Identification model development for proactive response on irregular operations (IROPs)," Journal of Air Transport Management, Elsevier, vol. 75(C), pages 1-8.
    19. Yuhe Shi & Zhenggang He, 2018. "Decision Analysis of Disturbance Management in the Process of Medical Supplies Transportation after Natural Disasters," IJERPH, MDPI, vol. 15(8), pages 1-18, August.
    20. Liang, Zhe & Feng, Yuan & Zhang, Xiaoning & Wu, Tao & Chaovalitwongse, Wanpracha Art, 2015. "Robust weekly aircraft maintenance routing problem and the extension to the tail assignment problem," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 238-259.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:11:p:2577-:d:1163662. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.