Assortment Optimization Under General Choice

51 Pages Posted: 22 Oct 2014 Last revised: 5 Mar 2016

See all articles by Srikanth Jagabathula

Srikanth Jagabathula

New York University (NYU) - Department of Information, Operations, and Management Sciences

Date Written: October 21, 2014

Abstract

We consider the key operational problem of optimizing the mix of offered products to maximize revenues when product prices are exogenously set and product demand follows a general discrete choice model. The key challenge in making this decision is the computational difficulty of finding the best subset, which often requires exhaustive search. Existing approaches address the challenge by either deriving efficient algorithms for specific parametric structures or studying the performance of general-purpose heuristics. The former approach results in algorithms that lack portability to other structures; whereas the latter approach has resulted in algorithms with poor performance. We study a portable and easy-to-implement local search heuristic. We show that it efficiently finds the global optimum for the multinomial logit (MNL) model and derive performance guarantees for general choice structures. Empirically, it is better than prevailing heuristics when no efficient algorithms exist, and it is within 0.02% of optimality otherwise.

Keywords: assortment optimization, choice models, local search, nonparametric choice models

Suggested Citation

Jagabathula, Srikanth, Assortment Optimization Under General Choice (October 21, 2014). Available at SSRN: https://ssrn.com/abstract=2512831 or http://dx.doi.org/10.2139/ssrn.2512831

Srikanth Jagabathula (Contact Author)

New York University (NYU) - Department of Information, Operations, and Management Sciences ( email )

44 West Fourth Street
New York, NY 10012
United States

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