Optimal Algorithms for Assortment Selection Under Ranking-Based Consumer Choice Models

Abstract

DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

Assortment Optimization and Pricing Under a Nonparametric Tree Choice Model

“…The tree model that we present should be viewed as a generalization of the intree and outtree models introduced in Honhon et al (2012). These models are similar to ours in that customer classes correspond to paths in an underlying tree.…”

Assortment Planning: Review of Literature and Industry Practice

“…They develop efficient, pseudopolynomial time algorithms to solve the resulting assortment optimization problem. Honhon et al (2012) study the optimal assortment problem under the assumption that (a) customers can be characterized into types based on a rank-ordered list of products they are willing to purchase, (b) proportion of consumers of each type is random and (c) purchases are dynamic, consumer-driven and stockout based. Following Honhon et al (2010), the authors relax the assumption of random proportions to show that the expected profits for the resulting fixed proportions model (FP) can be used to construct tight bounds on the expected profits for the random proportions model.…”

Assortment Optimization Under a Single Transition Model

“…There are many subsequent works studying the assortment optimization problem under various consumer choice models. Some examples include the nested logit model (see, e.g., Li and Rusmevichientong 2014, Davis et al 2014, Li et al 2015; the mixture of multinomial logit (MMNL) model (see, e.g., Goyal 2014, Rusmevichientong et al 2014), the rankingbased models (see, e.g., Honhon et al 2012, Aouad et al 2016, Bertsimas and Mišić 2016, the Mallows model (see e.g., Desir et al 2016b, Desir et al 2016a) and more recently, more complicated choice models such as the consider-then-choose model Aouad et al (2015a), the MNL model with endogenous network effects Wang and Wang (2016), choice model when consumer searches for product information Sahin and Wang (2016), etc. In another direction, there has also been interest in studying assortment optimization problems under nonparametric choice models (see Farias et al 2013, Jagabathula 2016 or in a dynamic environment (see Rusmevichientong et al 2010, ?, ?, Aouad et al 2015b.…”