Technical Note—New Bounds for Cardinality-Constrained Assortment Optimization Under the Nested Logit Model

Abstract

Assortment optimization involves determining the optimal set of products to show customers and is a fundamental problem in retail operations. The nested logit choice model is a popular and widely used choice model to capture customer behavior. In “New Bounds for Cardinality-Constrained Assortment Optimization Under the Nested Logit Model,” Kunnumkal presents a new method for making the assortment decisions under the nested logit choice model when there is a constraint on the number of products that can be offered within each nest. Computational experiments reveal that the assortments obtained by the solution method are near optimal, with the average optimality gap being under 1%.