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Computing booking limits under a non-parametric demand model: A mathematical programming approach

Author

Listed:
  • Morad Hosseinalifam

    (ExPretio Technologies)

  • Gilles Savard
  • Patrice Marcotte

Abstract

In revenue management, booking limits are commonly used to restrict access to classes of products, to subsequently make way for more profitable ones. Frequently, this inventory control policy assumes that products are nested in decreasing order of revenue, and that less profitable products are denied access first. In this article, we propose for the computation of nested booking limits a flexible mathematical programming framework that can accommodate business rules and technical constraints common in the industry. Under the assumption that customers are characterized by ordered lists of their preferences, optimal policies are set through the solution of a mixed integer program that is amenable to an efficient and scalable column generation algorithm. Numerical tests illustrate the improved performance of the resulting policies, which are numerically tested against alternative proposals from the current literature.

Suggested Citation

  • Morad Hosseinalifam & Gilles Savard & Patrice Marcotte, 2016. "Computing booking limits under a non-parametric demand model: A mathematical programming approach," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(2), pages 170-184, April.
    • Handle: RePEc:pal:jorapm:v:15:y:2016:i:2:d:10.1057_rpm.2015.47
    DOI: 10.1057/rpm.2015.47
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    References listed on IDEAS

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    1. Vivek F. Farias & Srikanth Jagabathula & Devavrat Shah, 2013. "A Nonparametric Approach to Modeling Choice with Limited Data," Management Science, INFORMS, vol. 59(2), pages 305-322, December.
    2. Garrett van Ryzin & Gustavo Vulcano, 2008. "Simulation-Based Optimization of Virtual Nesting Controls for Network Revenue Management," Operations Research, INFORMS, vol. 56(4), pages 865-880, August.
    3. Qian Liu & Garrett van Ryzin, 2008. "On the Choice-Based Linear Programming Model for Network Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 10(2), pages 288-310, October.
    4. Garrett van Ryzin & Gustavo Vulcano, 2008. "Computing Virtual Nesting Controls for Network Revenue Management Under Customer Choice Behavior," Manufacturing & Service Operations Management, INFORMS, vol. 10(3), pages 448-467, October.
    5. Juan José Miranda Bront & Isabel Méndez-Díaz & Gustavo Vulcano, 2009. "A Column Generation Algorithm for Choice-Based Network Revenue Management," Operations Research, INFORMS, vol. 57(3), pages 769-784, June.
    6. Chen, Lijian & Homem-de-Mello, Tito, 2010. "Mathematical programming models for revenue management under customer choice," European Journal of Operational Research, Elsevier, vol. 203(2), pages 294-305, June.
    7. Juan M. Chaneton & Gustavo Vulcano, 2011. "Computing Bid Prices for Revenue Management Under Customer Choice Behavior," Manufacturing & Service Operations Management, INFORMS, vol. 13(4), pages 452-470, October.
    8. Dimitris Bertsimas & Sanne de Boer, 2005. "Simulation-Based Booking Limits for Airline Revenue Management," Operations Research, INFORMS, vol. 53(1), pages 90-106, February.
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    Cited by:

    1. Barbier, Thibault & Anjos, Miguel F. & Cirinei, Fabien & Savard, Gilles, 2020. "Product-closing approximation for ranking-based choice network revenue management," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1002-1017.

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