The multilocation multiperiod inventory problem: bounds and approximations
Management Science
Dynamic Programming and Optimal Control, Two Volume Set
Dynamic Programming and Optimal Control, Two Volume Set
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Management of Multi-Item Retail Inventory Systems with Demand Substitution
Operations Research
Stocking Retail Assortments Under Dynamic Consumer Substitution
Operations Research
Relaxations of Weakly Coupled Stochastic Dynamic Programs
Operations Research
Computing an index policy for multiarmed bandits with deadlines
Proceedings of the 3rd International Conference on Performance Evaluation Methodologies and Tools
Dynamic Pricing for Nonperishable Products with Demand Learning
Operations Research
Inventory Management of a Fast-Fashion Retail Network
Operations Research
The Impact of Quick Response in Inventory-Based Competition
Manufacturing & Service Operations Management
Indexability of bandit problems with response delays
Probability in the Engineering and Informational Sciences
Computing a Classic Index for Finite-Horizon Bandits
INFORMS Journal on Computing
The Irrevocable Multiarmed Bandit Problem
Operations Research
Production risk management system with demand probability distribution
Advanced Engineering Informatics
Multiechelon Procurement and Distribution Policies for Traded Commodities
Management Science
Product and Price Competition with Satiation Effects
Management Science
Learning Consumer Tastes Through Dynamic Assortments
Operations Research
Computing a Classic Index for Finite-Horizon Bandits
INFORMS Journal on Computing
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
Fast fashion sales forecasting with limited data and time
Decision Support Systems
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Companies such as Zara and World Co. have recently implemented novel product development processes and supply chain architectures enabling them to make more product design and assortment decisions during the selling season, when actual demand information becomes available. How should such retail firms modify their product assortment over time in order to maximize overall profits for a given selling season? Focusing on a stylized version of this problem, we study a finite horizon multiarmed bandit model with several plays per stage and Bayesian learning. Our analysis involves the Lagrangian relaxation of weakly coupled dynamic programs (DPs), results contributing to the emerging theory of DP duality, and various approximations. It yields a closed-form dynamic index policy capturing the key exploration versus exploitation trade-off and associated suboptimality bounds. In numerical experiments its performance proves comparable to that of other closed-form heuristics described in the literature, but this policy is particularly easy to implement and interpret. This last feature enables extensions to more realistic versions of the motivating dynamic assortment problem that include implementation delays, switching costs, and demand substitution effects.