Revenue Management Under a General Discrete Choice Model of Consumer Behavior

Abstract

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 yield management problem in which the buyers' choice behavior is modeled explicitly. The choice model is perfectly general and simply specifies the probability of purchasing each fare product as 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 is of a simple form. Namely, it consists of 1) identifying the ordered family of "nondominated" subsets S 1 , ..., S m , and 2) at each point in time opening one of these sets S k , where the optimal index k is increasing in the remaining capacity x. That is, the more capacity we have available, the further the optimal set is along this sequence. Moreover, we show that the optimal policy is nested if and only if the ordered sets are increasing, that is S 1 ⊆ S 2 ⊆ ... ⊆ S n , and we give a complete characterization of when nesting by fare order is optimal. We then show that two important models, the independent demand model and the multinomial logit model (MNL), satisfy this later condition and hence nested-by-fare-order policies are optimal in these cases. 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.

Robust Assortment Optimization in Revenue Management Under the Multinomial Logit Choice Model

“…In this case, the firm needs to decide which assortment of products to offer, taking into account the choice process of the customers. The seminal work of Talluri and van Ryzin (2004) has demonstrated the importance of incorporating customer choice behavior into revenue management models. Since their formulation of the single leg airline seat allocation problem under a general discrete choice model, there has been a large amount of research on choice-based revenue management models.…”

“…When the parameters of the logit model are known in advance, Talluri and van Ryzin (2004) show that assortments of the form {1, 2, . .…”

“…This result shows that we can compute the robust assortment efficiently because we only need to consider n revenue-ordered assortments, instead of all 2 n subsets. In addition to generalizing the result of Talluri and van Ryzin (2004), our proof of Theorem 3.2 is novel and different from the existing techniques in the literature, contributing to a new understanding of the logit model (Lemma 3.1).…”

Assortment Optimization under Consumer Choice Behavior in Online Retailing

“…It is shown that profit-ordered assortments are optimal for this problem (Talluri and van Ryzin, 2004;Rusmevichientong and Topaloglu, 2012). This means that an optimal assortment is { } 1, 2, , * k for some *.…”

Integrated schedule planning with supply-demand interactions for a new generation of aircrafts

“…[5] integrate discrete choice modeling into the single-leg, multiple-fare-class revenue management model that determines the subset of fare products to offer at each point in time. [4] develops a market-oriented integrated schedule design and fleet assignment model with integrated pricing decisions.…”