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Multiple fleet aircraft schedule recovery following hub closures

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  • Thengvall, Benjamin G.
  • Yu, Gang
  • Bard, Jonathan F.

Abstract

This paper presents three multi-commodity network-type models for determining a recovery schedule for all aircraft operated by a large carrier following a hub closure. The first is a pure network with side constraints, the second is a generalized network, and the third is a pure network with side constraints in which the time horizon is discretized. Each model allows for cancellations, delays, ferry flights, and substitution between fleets and subfleets. In the first two cases, the objective is to maximize a "profit" function which includes an incentive to maintain as much of the original aircraft routings as possible. In the third case, the objective is to minimize the sum of cancellation and delay costs. After comparing solution quality and computation times for each of the three models, the first was seen to outperform the others and was singled out for further analysis. Results for a comprehensive set of scenarios are presented along with ideas for continuing work.

Suggested Citation

  • Thengvall, Benjamin G. & Yu, Gang & Bard, Jonathan F., 2001. "Multiple fleet aircraft schedule recovery following hub closures," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(4), pages 289-308, May.
    • Handle: RePEc:eee:transa:v:35:y:2001:i:4:p:289-308
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Nicholas G. Rupp & George M. Holmes & Jeff DeSimone, 2005. "Airline Schedule Recovery after Airport Closures: Empirical Evidence since September 11," Southern Economic Journal, John Wiley & Sons, vol. 71(4), pages 800-820, April.
    2. Nicholas G. Rupp & George M. Holmes & Jeff DeSimone, "undated". "Airline Schedule Recovery after Airport Closures: Empirical Evidence since September 11th," Working Papers 0207, East Carolina University, Department of Economics.
    3. Wenkai Li & Mark Wallace, 2012. "Disruption Management for Commercial Aviation," Working Papers EMS_2012_18, Research Institute, International University of Japan.
    4. Lavanya Marla & Bo Vaaben & Cynthia Barnhart, 2017. "Integrated Disruption Management and Flight Planning to Trade Off Delays and Fuel Burn," Transportation Science, INFORMS, vol. 51(1), pages 88-111, February.
    5. 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.
    6. Alderighi, Marco & Gaggero, Alberto A., 2018. "Flight cancellations and airline alliances: Empirical evidence from Europe," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 90-101.
    7. 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.
    8. S Yan & C-H Tang & C-H Chen, 2008. "Reassignments of common-use check-in counters following airport incidents," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(8), pages 1100-1108, August.
    9. Vaaben, Bo & Larsen, Jesper, 2015. "Mitigation of airspace congestion impact on airline networks," Journal of Air Transport Management, Elsevier, vol. 47(C), pages 54-65.
    10. 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.
    11. Bard, Jonathan F. & Mohan, Dinesh Natarajan, 2008. "Reallocating arrival slots during a ground delay program," Transportation Research Part B: Methodological, Elsevier, vol. 42(2), pages 113-134, February.
    12. G Zhu & J F Bard & G Yu, 2005. "Disruption management for resource-constrained project scheduling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(4), pages 365-381, April.
    13. 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.
    14. van Lieshout, R.N. & Mulder, J. & Huisman, D., 2016. "The Vehicle Rescheduling Problem with Retiming," Econometric Institute Research Papers EI2016-37, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    15. Zhao, Ai & Bard, Jonathan F. & Bickel, J. Eric, 2023. "A two-stage approach to aircraft recovery under uncertainty," Journal of Air Transport Management, Elsevier, vol. 111(C).
    16. 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).
    17. Brouer, Berit D. & Dirksen, Jakob & Pisinger, David & Plum, Christian E.M. & Vaaben, Bo, 2013. "The Vessel Schedule Recovery Problem (VSRP) – A MIP model for handling disruptions in liner shipping," European Journal of Operational Research, Elsevier, vol. 224(2), pages 362-374.

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