Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Stocking Retail Assortments Under Dynamic Consumer Substitution
Operations Research
Sampling-based Approximation Algorithms for Multi-stage Stochastic
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Dynamic Assortment with Demand Learning for Seasonal Consumer Goods
Management Science
Provably Near-Optimal Sampling-Based Policies for Stochastic Inventory Control Models
Mathematics of Operations Research
On the Choice-Based Linear Programming Model for Network Revenue Management
Manufacturing & Service Operations Management
A Column Generation Algorithm for Choice-Based Network Revenue Management
Operations Research
A PTAS for capacitated sum-of-ratios optimization
Operations Research Letters
Learning Consumer Tastes Through Dynamic Assortments
Operations Research
A Nonparametric Approach to Modeling Choice with Limited Data
Management Science
Optimal Dynamic Assortment Planning with Demand Learning
Manufacturing & Service Operations Management
A branch-and-cut algorithm for the latent-class logit assortment problem
Discrete Applied Mathematics
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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.