Assortment optimization under the Sequential Multinomial Logit Model

Alvaro Flores, Gerardo Berbeglia and Pascal Van Hentenryck

European Journal of Operational Research, 2019, vol. 273, issue 3, 1052-1064

Abstract: We study the assortment optimization problem under the Sequential Multinomial Logit (SML), a discrete choice model that generalizes the Multinomial Logit (MNL). Under the SML model, products are partitioned into two levels, to capture differences in attractiveness, brand awareness and, or visibility of the products in the market. When a consumer is presented with an assortment of products, she first considers products in the first level and, if none of them is purchased, products in the second level are considered. This model is a special case of the Perception-Adjusted Luce Model (PALM) recently proposed by Echenique et al. (2018). It can explain many behavioral phenomena such as the attraction, compromise, similarity effects and choice overload which cannot be explained by the MNL model or any discrete choice model based on random utility. In particular, the SML model allows violations to regularity which states that the probability of choosing a product cannot increase if the offer set is enlarged.

Keywords: Revenue management; Assortment optimization; Assortment planning; Discrete choice models; Revenue-ordered assortments (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:273:y:2019:i:3:p:1052-1064

DOI: 10.1016/j.ejor.2018.08.047

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