Speaker: Gerardo Berbeglia, Melbourne Business School - Melbourne University
The assortment problem in revenue management is the problem of deciding which subset of products to offer in order to maximize revenue. A simple strategy is to select the best assortment out of all those that are constructed by fixing a threshold revenue π and then choosing all products with revenue of at least π. This is known as the revenue-ordered assortments strategy. We study the approximation guarantees provided by revenue-ordered assortments when customers are rational in the following sense: the probability of selecting a specific product from the set being offered cannot increase if the offer set is enlarged. This rationality assumption, known as regularity, is satisfied by almost all discrete choice models including the general random utility model (RUM). The three bounds we obtain are tight.
In the second part of the talk, we present a new discrete choice model that generalizes the RUM. We show that this model, called the Generalized Stochastic Preference (GSP) model can explain several choice phenomena that can’t be represented by a RUM. In particular, the model can easily (and also exactly) replicate well-known choice experiments carried out since 1980’s that possess strong regularity violations. An appealing feature of the GSP is that it is non-parametric and therefore it has high flexibility.
The first part of the talk is joint work with Gwenaël Joret.