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A simulation scenario based mixed integer programming approach to airline reserve crew scheduling under uncertainty

Author

Listed:
  • Christopher Bayliss

    (ASAP, University of Southampton)

  • Geert Maere

    (ASAP, University of Southampton)

  • Jason A. D. Atkin

    (ASAP, University of Southampton)

  • Marc Paelinck

    (KLM Royal Dutch Airlines)

Abstract

The environment in which airlines operate is uncertain for many reasons, for example due to the effects of weather, traffic or crew unavailability (due to delay or sickness). This work focuses on airline reserve crew scheduling under crew absence uncertainty and delay for an airline operating a single hub and spoke network. Reserve crew can be used to cover absent crew or delayed connecting crew. A fixed number of reserve crew are available for scheduling and each requires a daily standby duty start time. This work proposes a mixed integer programming approach to scheduling the airline’s reserve crew. A simulation of the airline’s operations with stochastic journey time and crew absence inputs (without reserve crew) is used to generate input disruption scenarios for the mixed integer programming simulation scenario model (MIPSSM) formulation. Each disruption scenario corresponds to a record of all of the disruptions that may occur on the day of operation which are solvable by using reserve crew. A set of disruption scenarios form the input of the MIPSSM formulation, which has the objective of finding the reserve crew schedule that minimises the overall level of disruption over the set of input scenarios. Additionally, modifications of the MIPSSM are explored, a heuristic solution approach and a reserve use policy derived from the MIPSSM are introduced. A heuristic based on the proposed MIPSSM outperforms a range of alternative approaches. The heuristic solution approach suggests that including the right disruption scenarios is as important as the quantity of disruption scenarios that are added to the MIPSSM. An investigation into what makes a good set of scenarios is also presented.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:252:y:2017:i:2:d:10.1007_s10479-016-2174-8
    DOI: 10.1007/s10479-016-2174-8
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    References listed on IDEAS

    as
    1. Ahmed Abdelghany & Goutham Ekollu & Ram Narasimhan & Khaled Abdelghany, 2004. "A Proactive Crew Recovery Decision Support Tool for Commercial Airlines During Irregular Operations," Annals of Operations Research, Springer, vol. 127(1), pages 309-331, March.
    2. Jeffrey E. Dillon & Spyros Kontogiorgis, 1999. "US Airways Optimizes the Scheduling of Reserve Flight Crews," Interfaces, INFORMS, vol. 29(5), pages 123-131, October.
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    Cited by:

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    2. 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.
    3. Choi, Tsan-Ming & Wen, Xin & Sun, Xuting & Chung, Sai-Ho, 2019. "The mean-variance approach for global supply chain risk analysis with air logistics in the blockchain technology era," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 178-191.
    4. Vojtech Graf & Dusan Teichmann & Michal Dorda & Lenka Kontrikova, 2021. "Dynamic Model of Contingency Flight Crew Planning Extending to Crew Formation," Mathematics, MDPI, vol. 9(17), pages 1-28, September.

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