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Robust Airline Fleet Assignment: Imposing Station Purity Using Station Decomposition

Author

Listed:
  • Barry C. Smith

    (Sabre Holdings, 3150 Sabre Drive, Southlake, Texas 76092)

  • Ellis L. Johnson

    (School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0205)

Abstract
Fleet assignment models (FAM) are used by many airlines to assign aircraft to flights in a schedule to maximize profit. Major airlines report that the use of FAM increases annual profits by more than $100 million. The results of FAM affect subsequent planning, marketing, and operational processes within the airline. Anticipating these processes and developing solutions favorable to them can further increase the benefits of FAM. We develop fleet assignment solutions that increase planning flexibility and reduce cost by imposing station purity, limiting the number of fleet types allowed to serve each airport in the schedule. We demonstrate that imposing station purity on the FAM can limit aircraft dispersion in the network and make solutions more robust relative to crew planning, maintenance planning, and operations. Because station purity can significantly degrade computational efficiency, we develop a solution approach, station decomposition, which takes advantage of airline network structure. Station decomposition uses a column generation approach to solving the fleet assignment problem; we further improve the performance of station decomposition by developing a primal-dual method that increases solution quality and model efficiency. Station decomposition solutions can be highly fractional; we develop a “fix-and-price” heuristic to efficiently find integer solutions to the fleet assignment problem. We estimate that the annual net benefit of station purity because of reduced maintenance and crew scheduling costs is greater than $100 million for a major U.S. domestic airline.

Suggested Citation

  • Barry C. Smith & Ellis L. Johnson, 2006. "Robust Airline Fleet Assignment: Imposing Station Purity Using Station Decomposition," Transportation Science, INFORMS, vol. 40(4), pages 497-516, November.
    • Handle: RePEc:inm:ortrsc:v:40:y:2006:i:4:p:497-516
    DOI: 10.1287/trsc.1060.0153
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    References listed on IDEAS

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    Cited by:

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    2. Scott E. Atkinson & Kamalini Ramdas & Jonathan W. Williams, 2016. "Robust Scheduling Practices in the U.S. Airline Industry: Costs, Returns, and Inefficiencies," Management Science, INFORMS, vol. 62(11), pages 3372-3391, November.
    3. Wang, Chun-Han & Zhang, Wenzhu & Dai, Yue & Lee, Yu-Ching, 2022. "Frequency competition among airlines on coordinated airports network," European Journal of Operational Research, Elsevier, vol. 297(2), pages 484-495.
    4. 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.
    5. Chunhua Gao & Ellis Johnson & Barry Smith, 2009. "Integrated Airline Fleet and Crew Robust Planning," Transportation Science, INFORMS, vol. 43(1), pages 2-16, February.
    6. 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.
    7. Jane Lee & Lavanya Marla & Alexandre Jacquillat, 2020. "Dynamic Disruption Management in Airline Networks Under Airport Operating Uncertainty," Transportation Science, INFORMS, vol. 54(4), pages 973-997, July.
    8. Ahmad Almuhtady & Seungchul Lee & Edwin Romeijn & Michael Wynblatt & Jun Ni, 2014. "A Degradation-Informed Battery-Swapping Policy for Fleets of Electric or Hybrid-Electric Vehicles," Transportation Science, INFORMS, vol. 48(4), pages 609-618, November.
    9. Michelle Dunbar & Gary Froyland & Cheng-Lung Wu, 2012. "Robust Airline Schedule Planning: Minimizing Propagated Delay in an Integrated Routing and Crewing Framework," Transportation Science, INFORMS, vol. 46(2), pages 204-216, May.
    10. Birolini, Sebastian & Jacquillat, Alexandre, 2023. "Day-ahead aircraft routing with data-driven primary delay predictions," European Journal of Operational Research, Elsevier, vol. 310(1), pages 379-396.
    11. Timothy L. Jacobs & Barry C. Smith & Ellis L. Johnson, 2008. "Incorporating Network Flow Effects into the Airline Fleet Assignment Process," Transportation Science, INFORMS, vol. 42(4), pages 514-529, November.
    12. Jonathan Turner & Soonhui Lee & Mark Daskin & Tito Homem-de-Mello & Karen Smilowitz, 2012. "Dynamic fleet scheduling with uncertain demand and customer flexibility," Computational Management Science, Springer, vol. 9(4), pages 459-481, November.
    13. Liang, Zhe & Feng, Yuan & Zhang, Xiaoning & Wu, Tao & Chaovalitwongse, Wanpracha Art, 2015. "Robust weekly aircraft maintenance routing problem and the extension to the tail assignment problem," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 238-259.
    14. Hanif D. Sherali & Xiaomei Zhu, 2008. "Two-Stage Fleet Assignment Model Considering Stochastic Passenger Demands," Operations Research, INFORMS, vol. 56(2), pages 383-399, April.
    15. 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.
    16. Keji Wei & Vikrant Vaze, 2018. "Modeling Crew Itineraries and Delays in the National Air Transportation System," Transportation Science, INFORMS, vol. 52(5), pages 1276-1296, October.
    17. Gary Froyland & Stephen J. Maher & Cheng-Lung Wu, 2014. "The Recoverable Robust Tail Assignment Problem," Transportation Science, INFORMS, vol. 48(3), pages 351-372, August.

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