The exponomial choice model for assortment optimization: an alternative to the MNL model?

Aouad, A, Feldman, J and Segev, D (2023) The exponomial choice model for assortment optimization: an alternative to the MNL model? Management Science, 69 (5). pp. 2814-2832. ISSN 0025-1909


In this paper, we consider the yet-uncharted assortment optimization problem under the Exponomial choice model, where the objective is to determine the revenue maximizing set of products that should be offered to customers. Our main algorithmic contribution comes in the form of a fully polynomial-time approximation scheme (FPTAS), showing that the optimal expected revenue can be efficiently approached within any degree of accuracy. This result is obtained through a synthesis of ideas related to approximate dynamic programming, that enable us to derive a compact discretization of the continuous state space by keeping track of several key statistics in "rounded" form throughout the overall computation. Consequently, we obtain the first provably-good algorithm for assortment optimization under the Exponomial choice model, which is complemented by a number of hardness results for natural extensions. We show in computational experiments that our solution method admits an efficient implementation, based on additional pruning criteria. Furthermore, in light of recent empirical evidence in this context, we evaluate the Exponomial choice model from a data-driven perspective. We present two case studies, the first of which revolves around a contemporary setting, where users of a transit app are selecting from amongst a collection of potential travel modes, and the second is more traditional in nature, being focused on a purchase data setting in retail. We find the prediction power and accuracy of the Exponomial choice model in both studies to be on par with those produced by the Multinomial Logit model.

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Item Type: Article
Subject Areas: Management Science and Operations
Date Deposited: 01 Aug 2022 12:44
Date of first compliant deposit: 10 Aug 2022
Last Modified: 08 Jun 2024 00:44

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