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A Proactive Crew Recovery Decision Support Tool for Commercial Airlines During Irregular Operations

Author

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  • Ahmed Abdelghany
  • Goutham Ekollu
  • Ram Narasimhan
  • Khaled Abdelghany

Abstract

In this paper, a decision support tool that automates crew recovery during irregular operations for large-scale commercial airlines is presented. The tool is designed for airlines that adopt the hub-spoke network stru cture. The advance of this tool over the existing ones is that it recovers projected crew problems that arise due to current system disruptions. In other words, it proactively recovers crew problems ahead of time before their occurrence. In addition, it gives a wide flexibility to react to different operation scenarios. Also, it solves for the most efficient crew recovery plan with the least deviation from the originally planned schedule. The tool adopts a rolling approach in which a sequence of optimization assignment problems is solved such that it recovers flights in chronological order of their departure times. In each assignment problem, the objective is to recover as many flights as possible while minimizing total system cost resulting from resource reassignments and flight delays. The output of this tool is in the form of new crew trippairs that cover flights in the considered horizon. A test case is presented to illustrate the model capabilities to solve a real-life problem for one of the major commercial airlines in the U.S. Copyright Kluwer Academic Publishers 2004

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:127:y:2004:i:1:p:309-331:10.1023/b:anor.0000019094.19940.41
    DOI: 10.1023/B:ANOR.0000019094.19940.41
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    Citations

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    Cited by:

    1. Daniel Potthoff & Dennis Huisman & Guy Desaulniers, 2010. "Column Generation with Dynamic Duty Selection for Railway Crew Rescheduling," Transportation Science, INFORMS, vol. 44(4), pages 493-505, November.
    2. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    3. Ahmet Herekoğlu & Özgür Kabak, 2024. "Crew recovery optimization with deep learning and column generation for sustainable airline operation management," Annals of Operations Research, Springer, vol. 342(1), pages 399-427, November.
    4. Zhang, Dong & Yu, Chuhang & Desai, Jitamitra & Lau, H.Y.K. Henry, 2016. "A math-heuristic algorithm for the integrated air service recovery," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 211-236.
    5. Jonas Ingels & Broos Maenhout, 2017. "Employee substitutability as a tool to improve the robustness in personnel scheduling," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(3), pages 623-658, July.
    6. 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.
    7. 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.
    8. Abdelghany, Khaled F. & Abdelghany, Ahmed F. & Ekollu, Goutham, 2008. "An integrated decision support tool for airlines schedule recovery during irregular operations," European Journal of Operational Research, Elsevier, vol. 185(2), pages 825-848, March.
    9. 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.
    10. Ingels, Jonas & Maenhout, Broos, 2019. "Optimised buffer allocation to construct stable personnel shift rosters," Omega, Elsevier, vol. 82(C), pages 102-117.
    11. Abdelghany, Khaled F. & S. Shah, Sharmila & Raina, Sidhartha & Abdelghany, Ahmed F., 2004. "A model for projecting flight delays during irregular operation conditions," Journal of Air Transport Management, Elsevier, vol. 10(6), pages 385-394.
    12. Federico Malucelli & Emanuele Tresoldi, 2019. "Delay and disruption management in local public transportation via real-time vehicle and crew re-scheduling: a case study," Public Transport, Springer, vol. 11(1), pages 1-25, June.
    13. Jonas Ingels & Broos Maenhout, 2018. "The impact of overtime as a time-based proactive scheduling and reactive allocation strategy on the robustness of a personnel shift roster," Journal of Scheduling, Springer, vol. 21(2), pages 143-165, April.

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