2011/08/17 by Vivek F. Farias, Farias, Vivek, Srikanth Jagabathula +3
Business, Management and Accounting · Decision Sciences · #Auction Theory and Applications #Consumer Market Behavior and Pricing #FOS: Computer and information sciences #Methodology (stat.ME) #Supply Chain and Inventory Management
paper · pdf · doi:10.48550/arxiv.1108.3596
openalex publication_date 2011/08/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider the problem of static assortment optimization, where the goal is to find the assortment of size at most C that maximizes revenues. This is a fundamental decision problem in the area of Operations Management. It has been shown that this problem is provably hard for most of the important families of parametric of choice models, except the multinomial logit (MNL) model. In addition, most of the approximation schemes proposed in the literature are tailored to a specific parametric structure. We deviate from this and propose a general algorithm to find the optimal assortment assuming access to only a subroutine that gives revenue predictions; this means that the algorithm can be applied with any choice model. We prove that when the underlying choice model is the MNL model, our algorithm can find the optimal assortment efficiently.