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Yield Management Impacts on Airline Spill Estimation

Author

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  • Peter P. Belobaba

    (MIT Flight Transportation Laboratory, Cambridge, Massachusetts 02139)

  • András Farkas

    (MIT Flight Transportation Laboratory, Cambridge, Massachusetts 02139)

Abstract

The correct estimation of spill, or passenger demand turned away, is an integral part of the determination of optimal aircraft capacities in the airline fleet assignment process. While making advances in the solution of the large-scale fleet assignment optimization problem, airlines have continued to use an aggregate approach to spill estimation. This aggregate approach ignores the effects of yield management practices that have been widely implemented by airlines during the past decade. In this paper, we illustrate the importance of incorporating the effects of yield management booking limits into the methodology used to estimate both the number of passengers spilled at a given aircraft capacity and their associated revenue value. We describe an approach to spill estimation that makes use of the detailed demand information provided by yield management systems, and we present recursive algorithms that can be used to obtain more accurate spill estimates in cases when multiple booking classes are used. Numerical examples are presented to illustrate the extent to which the outcomes of the different estimation approaches differ, suggesting that these differences can be large enough to have an impact on optimal fleet assignment.

Suggested Citation

  • Peter P. Belobaba & András Farkas, 1999. "Yield Management Impacts on Airline Spill Estimation," Transportation Science, INFORMS, vol. 33(2), pages 217-232, May.
    • Handle: RePEc:inm:ortrsc:v:33:y:1999:i:2:p:217-232
    DOI: 10.1287/trsc.33.2.217
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    References listed on IDEAS

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    1. Renwick E. Curry, 1990. "Optimal Airline Seat Allocation with Fare Classes Nested by Origins and Destinations," Transportation Science, INFORMS, vol. 24(3), pages 193-204, August.
    2. Lawrence W. Robinson, 1995. "Optimal and Approximate Control Policies for Airline Booking with Sequential Nonmonotonic Fare Classes," Operations Research, INFORMS, vol. 43(2), pages 252-263, April.
    3. Richard D. Wollmer, 1992. "An Airline Seat Management Model for a Single Leg Route When Lower Fare Classes Book First," Operations Research, INFORMS, vol. 40(1), pages 26-37, February.
    4. Lawrence R. Weatherford & Samuel E. Bodily, 1992. "A Taxonomy and Research Overview of Perishable-Asset Revenue Management: Yield Management, Overbooking, and Pricing," Operations Research, INFORMS, vol. 40(5), pages 831-844, October.
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    Cited by:

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    2. Christina Büsing & Daniel Kadatz & Catherine Cleophas, 2019. "Capacity Uncertainty in Airline Revenue Management: Models, Algorithms, and Computations," Transportation Science, INFORMS, vol. 53(2), pages 383-400, March.
    3. König, Eva & Schön, Cornelia, 2021. "Railway delay management with passenger rerouting considering train capacity constraints," European Journal of Operational Research, Elsevier, vol. 288(2), pages 450-465.
    4. Hong Tsui, Kan Wai, 2017. "Does a low-cost carrier lead the domestic tourism demand and growth of New Zealand?," Tourism Management, Elsevier, vol. 60(C), pages 390-403.
    5. Jeffrey I. McGill & Garrett J. van Ryzin, 1999. "Revenue Management: Research Overview and Prospects," Transportation Science, INFORMS, vol. 33(2), pages 233-256, May.
    6. Spyros Kontogiorgis, 2000. "Practical Piecewise-Linear Approximation for Monotropic Optimization," INFORMS Journal on Computing, INFORMS, vol. 12(4), pages 324-340, November.

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