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

• 成果类型: Article
• 署名作者: Rusmevichientong, Paat; Shen, Zuo-Jun Max; Shmoys, David B.
• 署名单位: Cornell University; Cornell University; University of California System; University of California Berkeley
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.1100.0866
• 发表日期: 2010
• 页码: 1666-1680
• 关键词: Network Revenue Management; efficient allocation rules; Multiarmed bandit problem; multiple plays

摘要

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.