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Integrated Disruption Management and Flight Planning to Trade Off Delays and Fuel Burn

Author

Listed:
  • Lavanya Marla

    (University of Illinois at Urbana–Champaign, Urbana, Illinois 61801)

  • Bo Vaaben

    (Ops-Analytics, 3060 Espergaerde, Denmark)

  • Cynthia Barnhart

    (Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

Abstract

In this paper we present a novel approach addressing airline delays and recovery. Airline schedule recovery involves making decisions during operations to minimize additional operating costs while getting back on schedule as quickly as possible. The mechanisms used include aircraft swaps, flight cancellations, crew swaps, reserve crews, and passenger rebookings. In this context, we introduce another mechanism, namely flight planning that enables flight speed changes. Flight planning is the process of determining flight plan(s) specifying the route of a flight, its speed, and its associated fuel burn. Our key idea in integrating flight planning and disruption management is to adjust the speeds of flights during operations, trading off flying time (and along with it, block time) and fuel burn; in combination with existing mechanisms, such as flight holds. Our goal is striking the right balance of fuel costs and passenger-related delay costs incurred by the airline. We present both exact and approximate models for integrated aircraft and passenger recovery with flight planning. From computational experiments on data provided by a European airline, we estimate that the ability of our approach to control (decrease or increase) flying time by trading off with fuel burn, as well as to hold downstream flights, results in reductions in passenger disruptions by approximately 66%–83%, accompanied by small increases in fuel burn of 0.152%–0.155% and a total cost savings of approximately 5.7%–5.9% for the airline, may be achieved compared to baseline approaches typically used in practice. We discuss the relative benefits of two mechanisms studied—specifically, flight speed changes and intentionally holding flight departures, and show significant synergies in applying these mechanisms. The results, compared with recovery without integrated flight planning, are because of increased swap possibilities during recovery, decreased numbers of flight cancellations, and fewer disruptions to passengers.

Suggested Citation

  • Lavanya Marla & Bo Vaaben & Cynthia Barnhart, 2017. "Integrated Disruption Management and Flight Planning to Trade Off Delays and Fuel Burn," Transportation Science, INFORMS, vol. 51(1), pages 88-111, February.
  • Handle: RePEc:inm:ortrsc:v:51:y:2017:i:1:p:88-111
    DOI: 10.1287/trsc.2015.0609
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    Cited by:

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    3. 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.
    4. 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.
    5. Huang, Zhouchun & Luo, Xiaodong & Jin, Xianfei & Karichery, Sureshan, 2022. "An iterative cost-driven copy generation approach for aircraft recovery problem," European Journal of Operational Research, Elsevier, vol. 301(1), pages 334-348.
    6. Li, Max Z. & Ryerson, Megan S., 2019. "Reviewing the DATAS of aviation research data: Diversity, availability, tractability, applicability, and sources," Journal of Air Transport Management, Elsevier, vol. 75(C), pages 111-130.
    7. Erdem, Furkan & Bilgiç, Taner, 2024. "Airline delay propagation: Estimation and modeling in daily operations," Journal of Air Transport Management, Elsevier, vol. 115(C).
    8. Evler, Jan & Asadi, Ehsan & Preis, Henning & Fricke, Hartmut, 2021. "Airline ground operations: Optimal schedule recovery with uncertain arrival times," Journal of Air Transport Management, Elsevier, vol. 92(C).
    9. Wang, Chunzheng & Hu, Minghua & Yang, Lei & Zhao, Zheng, 2022. "Improving the spatial-temporal generalization of flight block time prediction: A development of stacking models," Journal of Air Transport Management, Elsevier, vol. 103(C).
    10. Uğur Arıkan & Sinan Gürel & M. Selim Aktürk, 2017. "Flight Network-Based Approach for Integrated Airline Recovery with Cruise Speed Control," Transportation Science, INFORMS, vol. 51(4), pages 1259-1287, November.
    11. Huang, Lei & Xiao, Fan & Zhou, Jing & Duan, Zhenya & Zhang, Hua & Liang, Zhe, 2023. "A machine learning based column-and-row generation approach for integrated air cargo recovery problem," Transportation Research Part B: Methodological, Elsevier, vol. 178(C).
    12. Lei Kang & Mark Hansen, 2021. "Quantile Regression–Based Estimation of Dynamic Statistical Contingency Fuel," Transportation Science, INFORMS, vol. 55(1), pages 257-273, 1-2.
    13. Naz Yeti̇moğlu, Yücel & Selim Aktürk, M., 2021. "Aircraft and passenger recovery during an aircraft’s unexpected unavailability," Journal of Air Transport Management, Elsevier, vol. 91(C).

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