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Deadhead Selection for the Long-Haul Crew Pairing Problem

Author

Listed:
  • Cynthia Barnhart

    (Massachusetts Institute of Technology, Cambridge, Massachusetts)

  • Levent Hatay

    (American Airlines Decision Technologies, Dallas/Fort Worth Airport, Texas)

  • Ellis L. Johnson

    (Georgia Institute of Technology, Atlanta, Georgia)

Abstract

The long-haul crew pairing problem involves the assignment of crews to scheduled flights such that overall costs are minimized and crew availability and work rule restrictions are satisfied. These problems are characterized by international flights that typically do not operate on a daily schedule, resulting in a sparsity of flights and extended periods of inactivity for crews at some stations. To eliminate these extended rest periods and reduce overall costs, it is advantageous in some cases to deadhead crews, that is, to assign crews to flights as passengers for repositioning and better utilization. In this paper, a heuristic methodology is developed to improve crew pairing solutions through the efficient selection and utilization of deadhead flights. The methodology uses the dual solutions determined in solving the linear programming relaxation of the crew pairing problem to build arrival and departure profiles at each station. These profiles provide a mechanism to price-out potential deadhead flights. Flights that price-out favorably may be used to build improved solutions to the crew pairing problem. The Deadhead Selection Procedure is tested using data provided by a long-haul airline and is shown to achieve significant improvement in crew costs by reducing the total number of deadhead hours flown and by reducing the total duration of rest periods.

Suggested Citation

  • Cynthia Barnhart & Levent Hatay & Ellis L. Johnson, 1995. "Deadhead Selection for the Long-Haul Crew Pairing Problem," Operations Research, INFORMS, vol. 43(3), pages 491-499, June.
    • Handle: RePEc:inm:oropre:v:43:y:1995:i:3:p:491-499
    DOI: 10.1287/opre.43.3.491
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    Citations

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    Cited by:

    1. Balaji Gopalakrishnan & Ellis. Johnson, 2005. "Airline Crew Scheduling: State-of-the-Art," Annals of Operations Research, Springer, vol. 140(1), pages 305-337, November.
    2. Maenhout, Broos & Vanhoucke, Mario, 2010. "A hybrid scatter search heuristic for personalized crew rostering in the airline industry," European Journal of Operational Research, Elsevier, vol. 206(1), pages 155-167, October.
    3. 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.
    4. Yan, Shangyao & Tu, Yu-Ping, 2002. "A network model for airline cabin crew scheduling," European Journal of Operational Research, Elsevier, vol. 140(3), pages 531-540, August.
    5. 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.
    6. Frédéric Quesnel & Guy Desaulniers & Frédéric Quesnel, 2020. "Improving Air Crew Rostering by Considering Crew Preferences in the Crew Pairing Problem," Transportation Science, INFORMS, vol. 54(1), pages 97-114, January.
    7. Ernst, A. T. & Jiang, H. & Krishnamoorthy, M. & Sier, D., 2004. "Staff scheduling and rostering: A review of applications, methods and models," European Journal of Operational Research, Elsevier, vol. 153(1), pages 3-27, February.
    8. Jacques Desrosiers & Arielle Lasry & Daniel McInnis & Marius M. Solomon & François Soumis, 2000. "Air Transat Uses ALTITUDE to Manage Its Aircraft Routing, Crew Pairing, and Work Assignment," Interfaces, INFORMS, vol. 30(2), pages 41-53, April.
    9. 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).
    10. Sai Ho Chung & Hoi Lam Ma & Hing Kai Chan, 2017. "Cascading Delay Risk of Airline Workforce Deployments with Crew Pairing and Schedule Optimization," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1443-1458, August.
    11. Pan, Hanchuan & Liu, Zhigang & Yang, Lixing & Liang, Zhe & Wu, Qiang & Li, Sijie, 2021. "A column generation-based approach for integrated vehicle and crew scheduling on a single metro line with the fully automatic operation system by partial supervision," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    12. 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.
    13. Desaulniers, G. & Desrosiers, J. & Dumas, Y. & Marc, S. & Rioux, B. & Solomon, M. M. & Soumis, F., 1997. "Crew pairing at Air France," European Journal of Operational Research, Elsevier, vol. 97(2), pages 245-259, March.
    14. Jütte, Silke & Thonemann, Ulrich W., 2012. "Divide-and-price: A decomposition algorithm for solving large railway crew scheduling problems," European Journal of Operational Research, Elsevier, vol. 219(2), pages 214-223.
    15. Da Lu & Fatma Gzara, 2015. "The robust crew pairing problem: model and solution methodology," Journal of Global Optimization, Springer, vol. 62(1), pages 29-54, May.
    16. Wark, Peter & Holt, John & Ronnqvist, Mikael & Ryan, David, 1997. "Aircrew schedule generation using repeated matching," European Journal of Operational Research, Elsevier, vol. 102(1), pages 21-35, October.
    17. Yan, Shangyao & Chang, Jei-Chi, 2002. "Airline cockpit crew scheduling," European Journal of Operational Research, Elsevier, vol. 136(3), pages 501-511, February.
    18. Nissen, Rüdiger & Haase, Knut, 2004. "Duty-period-based network model for airline crew rescheduling," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 581, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.

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