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Prescriptive analytics in airline operations: Arrival time prediction and cost index optimization for short-haul flights

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  • Achenbach, Anna
  • Spinler, Stefan

Abstract

In this paper, we provide arrival time prediction combined with a cost index optimization model for short haul flights. Our work is based on flight data of a European network carrier. We focus on predicting the arrival time for incoming flights at two hub locations. Airlines focus on two aspects in their operations: Minimizing cost while ensuring on-time arrivals. Especially network carriers with hub connections need to ensure that incoming flights are on time for passenger, crew and aircraft transfer. The cost index is a tool for optimizing the aircraft’s speed. A high cost index implies a faster flight. The cost of time is set in relation to the cost of fuel. Today there is no model for arrival time prediction and integrated cost index optimization. We consider three different flight distances to model the impact of cost index changes on gate arrival time. With our model airlines are able to reduce the cost index without any tangible impact on their overall schedule. We conclude that the optimal cost index level heavily depends on a flight’s distance, fuel costs and delay costs. Especially for short haul flights we recommend lowering the cost index as a high cost index has limited impact on gate arrival time.

Suggested Citation

  • Achenbach, Anna & Spinler, Stefan, 2018. "Prescriptive analytics in airline operations: Arrival time prediction and cost index optimization for short-haul flights," Operations Research Perspectives, Elsevier, vol. 5(C), pages 265-279.
    • Handle: RePEc:eee:oprepe:v:5:y:2018:i:c:p:265-279
    DOI: 10.1016/j.orp.2018.08.004
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    References listed on IDEAS

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    1. Kim, Myung Suk, 2016. "Analysis of short-term forecasting for flight arrival time," Journal of Air Transport Management, Elsevier, vol. 52(C), pages 35-41.
    2. Campanelli, Bruno & Fleurquin, Pablo & Arranz, Andrés & Etxebarria, Izaro & Ciruelos, Carla & Eguíluz, Víctor M. & Ramasco, José J., 2016. "Comparing the modeling of delay propagation in the US and European air traffic networks," Journal of Air Transport Management, Elsevier, vol. 56(PA), pages 12-18.
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    4. Edwards, Holly A. & Dixon-Hardy, Darron & Wadud, Zia, 2016. "Aircraft cost index and the future of carbon emissions from air travel," Applied Energy, Elsevier, vol. 164(C), pages 553-562.
    5. Tu, Yufeng & Ball, Michael O. & Jank, Wolfgang S., 2008. "Estimating Flight Departure Delay DistributionsA Statistical Approach With Long-Term Trend and Short-Term Pattern," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 112-125, March.
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    Cited by:

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    2. van Schilt, Isabelle M. & van Kalker, Jonna & Lefter, Iulia & Kwakkel, Jan H. & Verbraeck, Alexander, 2024. "Buffer scheduling for improving on-time performance and connectivity with a multi-objective simulation–optimization model: A proof of concept for the airline industry," Journal of Air Transport Management, Elsevier, vol. 115(C).

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