Assortment Optimization Under General Choice

TL;DR

This paper introduces a general algorithm for static assortment optimization that can be applied with any choice model, efficiently finding optimal assortments under the MNL model and offering broad applicability.

Contribution

It proposes a versatile algorithm for assortment optimization that requires only revenue prediction subroutines, applicable across various choice models.

Findings

Efficiently finds optimal assortments under the MNL model.

Applicable to any choice model with revenue prediction capability.

Provides a general framework beyond parametric models.

Abstract

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.