Management of Multi-Item Retail Inventory Systems with Demand Substitution
Operations Research
Stocking Retail Assortments Under Dynamic Consumer Substitution
Operations Research
Dynamic Assortment with Demand Learning for Seasonal Consumer Goods
Management Science
Retail Assortment Planning in the Presence of Consumer Search
Manufacturing & Service Operations Management
On the Choice-Based Linear Programming Model for Network Revenue Management
Manufacturing & Service Operations Management
OR FORUM--Rocket Science Retailing: The 2006 Philip McCord Morse Lecture
Operations Research
A Column Generation Algorithm for Choice-Based Network Revenue Management
Operations Research
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We study the product assortment problem of a retail operation that faces a stream of customers who are heterogeneous with respect to preferences. Each customer belongs to a market segment characterized by a consideration set that includes the alternatives viewed as options, and by the preference weights that the segment assigns to each of those alternatives. Upon arrival, he checks the offer set displayed by the firm, and either chooses one of those products or quits without purchasing according to a multinomial-logit (MNL) criterion. The firm's goal is to maximize the expected revenue extracted during a fixed time horizon. This problem also arises in the growing area of choice-based, network revenue management, where computational speed is a critical factor for the practical viability of a solution approach. This so-called latent-class, logit assortment problem is known to be NP-Hard. In this paper, we analyze unconstrained and constrained (i.e., with a limited number of products to display) versions of it, and propose a branch-and-cut algorithm that is computationally fast and leads to (nearly) optimal solutions.