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Revenue Management Under a General Discrete Choice Model of Consumer Behavior

  • Kalyan Talluri

    ()

    (Department of Economics and Business, Universitat Pompeu Fabra, Ramon Trias Fargas 25-27, 08005 Barcelona, Spain)

  • Garrett van Ryzin

    ()

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

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    Customer choice behavior, such as buy-up and buy-down, is an important phenomenon in a wide range of revenue management contexts. Yet most revenue management methodologies ignore this phenomenon---or at best approximate it in a heuristic way. In this paper, we provide an exact and quite general analysis of this problem. Specifically, we analyze a single-leg reserve management problem in which the buyers' choice behavior is modeled explicitly. The choice model is very general, simply specifying the probability of purchase for each fare product as a function of the set of fare products offered. The control problem is to decide which subset of fare products to offer at each point in time. We show that the optimal policy for this problem has a quite simple form. Namely, it consists of identifying an ordered family of "efficient" subsets S 1 , ..., S m , and at each point in time opening one of these sets S k , where the optimal index k is increasing in the remaining capacity x and decreasing in the remaining time. That is, the more capacity (or less time) available, the further the optimal set is along this sequence. We also show that the optimal policy is a nested allocation policy if and only if the sequence of efficient sets is nested, that is S 1 \subseteq S 2 \subseteq ... \subseteq S m . Moreover, we give a characterization of when nesting by fare order is optimal. We also develop an estimation procedure for this setting based on the expectation-maximization (EM) method that jointly estimates arrival rates and choice model parameters when no-purchase outcomes are unobservable. Numerical results are given to illustrate both the model and estimation procedure.

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    File URL: http://dx.doi.org/10.1287/mnsc.1030.0147
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    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 50 (2004)
    Issue (Month): 1 (January)
    Pages: 15-33

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    Handle: RePEc:inm:ormnsc:v:50:y:2004:i:1:p:15-33
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    1. Michael A. Crew & Paul R. Kleindorfer, 1976. "Peak Load Pricing with a Diverse Technology," Bell Journal of Economics, The RAND Corporation, vol. 7(1), pages 207-231, Spring.
    2. Guillermo Gallego & Garrett van Ryzin, 1994. "Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons," Management Science, INFORMS, vol. 40(8), pages 999-1020, August.
    3. Youyi Feng & Guillermo Gallego, 2000. "Perishable Asset Revenue Management with Markovian Time Dependent Demand Intensities," Management Science, INFORMS, vol. 46(7), pages 941-956, July.
    4. Dror, Moshe & Trudeau, Pierre & Ladany, Shaul P., 1988. "Network models for seat allocation on flights," Transportation Research Part B: Methodological, Elsevier, vol. 22(4), pages 239-250, August.
    5. Kalyan Talluri & Garrett van Ryzin, 1998. "An Analysis of Bid-Price Controls for Network Revenue Management," Management Science, INFORMS, vol. 44(11-Part-1), pages 1577-1593, November.
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