Stocking Retail Assortments Under Dynamic Consumer Substitution
Operations Research
Assortment Planning and Inventory Decisions Under a Locational Choice Model
Management Science
Dynamic Assortment with Demand Learning for Seasonal Consumer Goods
Management Science
Relative Entropy, Exponential Utility, and Robust Dynamic Pricing
Operations Research
Dynamic Pricing for Nonperishable Products with Demand Learning
Operations Research
Dynamic Pricing with a Prior on Market Response
Operations Research
Assortment Planning and Inventory Decisions Under Stockout-Based Substitution
Operations Research
The Irrevocable Multiarmed Bandit Problem
Operations Research
Learning Consumer Tastes Through Dynamic Assortments
Operations Research
Dynamic Pricing Under a General Parametric Choice Model
Operations Research
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We study a family of stylized assortment planning problems, where arriving customers make purchase decisions among offered products based on maximizing their utility. Given limited display capacity and no a priori information on consumers' utility, the retailer must select which subset of products to offer. By offering different assortments and observing the resulting purchase behavior, the retailer learns about consumer preferences, but this experimentation should be balanced with the goal of maximizing revenues. We develop a family of dynamic policies that judiciously balance the aforementioned trade-off between exploration and exploitation, and prove that their performance cannot be improved upon in a precise mathematical sense. One salient feature of these policies is that they “quickly” recognize, and hence limit experimentation on, strictly suboptimal products.