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Optimization models in RM systems: Optimality versus revenue gains

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  • Peter P Belobaba

    (MIT International Center for Air Transportation)

Abstract

Optimization models in airline revenue management (RM) systems have evolved from single flight leg to network revenue maximization to marginal revenue optimization for less restricted fare structures. This article reviews the most common optimization approaches that have been widely implemented in airline RM systems, with a focus on how the mismatch between model assumptions and reality can affect achievable revenue performance. Simulation findings from the Passenger Origin-Destination Simulator are used to illustrate how robustness and revenue gains, as opposed to theoretical optimality, have driven the widespread adoption of practical optimization models in RM systems.

Suggested Citation

  • Peter P Belobaba, 2016. "Optimization models in RM systems: Optimality versus revenue gains," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(3), pages 229-235, July.
    • Handle: RePEc:pal:jorapm:v:15:y:2016:i:3:d:10.1057_rpm.2016.13
    DOI: 10.1057/rpm.2016.13
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    References listed on IDEAS

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    Cited by:

    1. Ernst Ahlberg & Irina Mirkina & Alfred Olsson & Christian Söyland & Lars Carlsson, 2023. "On the selection of relevant historical demand data for revenue management applied to transportation," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(4), pages 266-275, August.
    2. Alderighi, Marco & Gaggero, Alberto A. & Piga, Claudio A., 2022. "Hidden prices with fixed inventory: Evidence from the airline industry," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 42-61.
    3. Thomas Fiig & Larry R. Weatherford & Michael D. Wittman, 2019. "Can demand forecast accuracy be linked to airline revenue?," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(4), pages 291-305, August.
    4. Michael D. Wittman & Thomas Fiig & Peter P. Belobaba, 2018. "A dynamic pricing engine for multiple substitutable flights," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 17(6), pages 420-435, December.
    5. Michael D. Wittman & Peter P. Belobaba, 2018. "Customized dynamic pricing of airline fare products," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 17(2), pages 78-90, April.
    6. Syed A. M. Shihab & Peng Wei, 2022. "A deep reinforcement learning approach to seat inventory control for airline revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(2), pages 183-199, April.

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