Assortment and Price Optimization Under the Two-Stage Luce model

Alvaro Flores, Gerardo Berbeglia, Pascal Van Hentenryck

This paper studies assortment and pricing optimization problems under the Two-Stage Luce model (2SLM), a discrete choice model introduced by Echenique and Saito (2018) that generalizes the multinomial logit model (MNL). The model employs an utility function as in the the MNL, and a dominance relation between products. When consumers are offered an assortmentS , they first discard all dominated products in S and then select one of the remaining products using the standard MNL. This model may violate the regularity condition, which states that the probability of choosing a product cannot increase if the offer set is enlarged. Therefore, the 2SLM falls outside the large family of discrete choice models based on random utility which contains almost all choice models studied in revenue management. We prove that the assortment problem under the 2SLM is polynomial-time solvable. Moreover, we show that the capacitated assortment optimization problem is NP-hard and but it admits polynomial-time algorithms for the relevant special cases cases where (1) the dominance relation is attractiveness-correlated and (2) its transitive reduction is a forest. The proofs exploit a strong connection between assortments under the 2SLM and independent sets in comparability graphs. Finally, we study the associated joint pricing and assortment problem under this model. First, we show that well known optimal pricing policy for the MNL can be arbitrarily bad. Our main result in this section is the development of an efficient algorithm for this pricing problem. The resulting optimal pricing strategy is simple to describe: it assigns the same price for all products, except for the one with the highest attractiveness and as well as for the one with the lowest attractiveness.

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