Revenue management under general discrete choice model of consumer behavior
Customer choice behavior, such as 'buy-up' and 'buy-down', is an important phe-nomenon in a wide range of industries. Yet there are few models or methodologies available to exploit this phenomenon within yield management systems. We make some progress on filling this void. Specifically, we develop a model of yield management in which the buyers' behavior is modeled explicitly using a multi-nomial logit model of demand. The control problem is to decide which subset of fare classes to offer at each point in time. The set of open fare classes then affects the purchase probabilities for each class. We formulate a dynamic program to determine the optimal control policy and show that it reduces to a dynamic nested allocation policy. Thus, the optimal choice-based policy can easily be implemented in reservation systems that use nested allocation controls. We also develop an estimation procedure for our model based on the expectation-maximization (EM) method that jointly estimates arrival rates and choice model parameters when no-purchase outcomes are unobservable. Numerical results show that this combined optimization -estimation approach may significantly improve revenue performance relative to traditional leg-based models that do not account for choice behavior.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- 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.
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