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Computing Virtual Nesting Controls for Network Revenue Management Under Customer Choice Behavior

Author

Listed:
  • Garrett van Ryzin

    () (Graduate School of Business, Columbia University, New York, New York 10027)

  • Gustavo Vulcano

    () (Stern School of Business, New York University, New York, New York 10012)

Abstract

We consider a revenue management, network capacity control problem in a setting where heterogeneous customers choose among the various products offered by a firm (e.g., different flight times, fare classes, and/or routings). Customers may therefore substitute if their preferred products are not offered. These individual customer choice decisions are modeled as a very general stochastic sequence of customers, each of whom has an ordered list of preferences. Minimal assumptions are made about the statistical properties of this demand sequence. We assume that the firm controls the availability of products using a virtual nesting control strategy and would like to optimize the protection levels for its virtual classes accounting for the (potentially quite complex) choice behavior of its customers. We formulate a continuous demand and capacity approximation for this problem, which allows for the partial acceptance of requests for products. The model admits an efficient calculation of the sample path gradient of the network revenue function. This gradient is then used to construct a stochastic steepest ascent algorithm. We show the algorithm converges in probability to a stationary point of the expected revenue function under mild conditions. The algorithm is relatively efficient even on large network problems, and in our simulation experiments it produces significant revenue increases relative to traditional virtual nesting methods. On a large-scale, real-world airline example using choice behavior models fit to actual booking data, the method produced an estimated 10% improvement in revenue relative to the controls used by the airline. The examples also provide interesting insights into how protection levels should be adjusted to account for choice behavior. Overall, the results indicate that choice behavior has a significant impact on both capacity control decisions and revenue performance and that our method is a viable approach for addressing the problem.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:ormsom:v:10:y:2008:i:3:p:448-467
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    File URL: http://dx.doi.org/10.1287/msom.1070.0210
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    References listed on IDEAS

    as
    1. Kalyan Talluri & Garrett van Ryzin, 2004. "Revenue Management Under a General Discrete Choice Model of Consumer Behavior," Management Science, INFORMS, vol. 50(1), pages 15-33, January.
    2. Andersson, Sven-Eric, 1989. "Operational planning in airline business -- Can science improve efficiency? Experiences from SAS," European Journal of Operational Research, Elsevier, vol. 43(1), pages 3-12, November.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Meissner, Joern & Strauss, Arne, 2012. "Network revenue management with inventory-sensitive bid prices and customer choice," European Journal of Operational Research, Elsevier, vol. 216(2), pages 459-468.
    2. Dupuis, Nicolas & Ivaldi, Marc & Pouyet, Jérôme, 2015. "A Welfare Assessment of Revenue Management Systems," CEPR Discussion Papers 10385, C.E.P.R. Discussion Papers.
    3. repec:eee:proeco:v:193:y:2017:i:c:p:352-364 is not listed on IDEAS
    4. Aslani, Shirin & Modarres, Mohammad & Sibdari, Soheil, 2014. "On the fairness of airlines’ ticket pricing as a result of revenue management techniques," Journal of Air Transport Management, Elsevier, vol. 40(C), pages 56-64.
    5. Meissner, Joern & Strauss, Arne, 2012. "Improved bid prices for choice-based network revenue management," European Journal of Operational Research, Elsevier, vol. 217(2), pages 417-427.
    6. Gustavo Vulcano & Garrett van Ryzin & Wassim Chaar, 2010. "OM Practice--Choice-Based Revenue Management: An Empirical Study of Estimation and Optimization," Manufacturing & Service Operations Management, INFORMS, vol. 12(3), pages 371-392, February.
    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. 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.

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