Dynamic Assortment Optimization with a Multinomial Logit Choice Model and Capacity Constraint

Paat Rusmevichientong (), Zuo-Jun Max Shen () and David B. Shmoys ()
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Paat Rusmevichientong: School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853
Zuo-Jun Max Shen: Department of Industrial Engineering and Operations Research, University of California--Berkeley, Berkeley, California 94720
David B. Shmoys: School of Operations Research and Information Engineering, and Department of Computer Science, Cornell University, Ithaca, New York 14853

Operations Research, 2010, vol. 58, issue 6, 1666-1680

Abstract: We consider an assortment optimization problem where a retailer chooses an assortment of products that maximizes the profit subject to a capacity constraint. The demand is represented by a multinomial logit choice model. We consider both the static and dynamic optimization problems. In the static problem, we assume that the parameters of the logit model are known in advance; we then develop a simple algorithm for computing a profit-maximizing assortment based on the geometry of lines in the plane and derive structural properties of the optimal assortment. For the dynamic problem, the parameters of the logit model are unknown and must be estimated from data. By exploiting the structural properties found for the static problem, we develop an adaptive policy that learns the unknown parameters from past data and at the same time optimizes the profit. Numerical experiments based on sales data from an online retailer indicate that our policy performs well.

Keywords: assortment optimization; multinomial logit choice model; capacity constraint (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (106)

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