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Airline crew scheduling: models, algorithms, and data sets

Author

Listed:
  • Atoosa Kasirzadeh

    (GERAD and École Polytechnique de Montréal)

  • Mohammed Saddoune

    (GERAD and École Polytechnique de Montréal
    Université Hassan II, Faculté des Sciences et Techniques de Mohammedia)

  • François Soumis

    (GERAD and École Polytechnique de Montréal)

Abstract

The airline crew scheduling problem has received extensive attention, particularly in the last 60 years. This problem is frequently divided into crew pairing and crew assignment because of its large size and the complex safety agreements and contractual rules. Several solution methodologies have been developed, but many objectives and constraints are treated approximately and research is ongoing. In this paper, we present a comprehensive problem definition for the airline crew scheduling problem, and we review existing problem formulations and solution methodologies. In addition, we formulate the personalized cockpit crew scheduling problem as a set covering problem and we solve it using column generation. We present computational results for real data from a major US carrier, and we describe the data sets (available on the internet) in detail to establish a basis for future research.

Suggested Citation

  • Atoosa Kasirzadeh & Mohammed Saddoune & François Soumis, 2017. "Airline crew scheduling: models, algorithms, and data sets," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(2), pages 111-137, June.
    • Handle: RePEc:spr:eurjtl:v:6:y:2017:i:2:d:10.1007_s13676-015-0080-x
    DOI: 10.1007/s13676-015-0080-x
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    References listed on IDEAS

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    Cited by:

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    2. Quesnel, Frédéric & Desaulniers, Guy & Soumis, François, 2020. "A branch-and-price heuristic for the crew pairing problem with language constraints," European Journal of Operational Research, Elsevier, vol. 283(3), pages 1040-1054.
    3. Neufeld, Janis S. & Scheffler, Martin & Tamke, Felix & Hoffmann, Kirsten & Buscher, Udo, 2021. "An efficient column generation approach for practical railway crew scheduling with attendance rates," European Journal of Operational Research, Elsevier, vol. 293(3), pages 1113-1130.
    4. Zeighami, Vahid & Saddoune, Mohammed & Soumis, François, 2020. "Alternating Lagrangian decomposition for integrated airline crew scheduling problem," European Journal of Operational Research, Elsevier, vol. 287(1), pages 211-224.
    5. Y. Wang (Ying) & Z. Shang (Zheming) & Huisman, D. & D'Ariano, A. & J.C. Zhang (Jinchuan), 2018. "A Lagrangian Relaxation Approach Based on a Time-Space-State Network for Railway Crew Scheduling," Econometric Institute Research Papers EI2018-45, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Weihao Ouyang & Xiaohong Zhu, 2023. "Meta-Heuristic Solver with Parallel Genetic Algorithm Framework in Airline Crew Scheduling," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    7. Zeren, Bahadır & Özcan, Ender & Deveci, Muhammet, 2024. "An adaptive greedy heuristic for large scale airline crew pairing problems," Journal of Air Transport Management, Elsevier, vol. 114(C).
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    11. Doi, Tsubasa & Nishi, Tatsushi & Voß, Stefan, 2018. "Two-level decomposition-based matheuristic for airline crew rostering problems with fair working time," European Journal of Operational Research, Elsevier, vol. 267(2), pages 428-438.
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    15. Omar Foutlane & Issmail Hallaoui & Pierre Hansen, 2022. "Distributed Integral Column Generation for Set Partitioning Problems," SN Operations Research Forum, Springer, vol. 3(2), pages 1-22, June.
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