An Exact Method for (Constrained) Assortment Optimization Problems with Product Costs

TL;DR

This paper introduces the first exact method for assortment optimization with product costs under a multinomial logit model, efficiently solving instances with up to a thousand products.

Contribution

The paper presents a novel exact solution approach for constrained assortment optimization problems, improving computational efficiency over existing methods.

Findings

Optimal assortments can be found in about 0.2 seconds for large instances.

The bounding procedure provides tight bounds, enabling effective optimization.

The method adapts to capacity constraints and mixed-logit models, with significant speedups.

Abstract

We study the problem of optimizing assortment decisions in the presence of product-specific costs when customers choose according to a multinomial logit model. This problem is NP-hard and approximate solutions methods have been proposed in the literature to obtain both lower and upper bounds in a tractable manner. We propose the first exact solution method for this problem and show that provably optimal assortments of instances with up to one thousand products can be found, on average, in about two tenths of a second. In particular, we propose a bounding procedure based on the approximation method of Feldman and Topaloglu (2015a) to provide tight lower and upper bounds at a fraction of their computing times. We show how these bounds can be used to effectively identify an optimal assortment. We also describe how to adapt our approach to handle cardinality or space/resource capacity constraints on the assortment as well as assortment optimization under a mixed-multinomial logit model. In both cases, our solution method provides significant computational boosts compared to exact methods from the literature.