Asymptotic Behavior of an Allocation Policy for Revenue Management
Operations Research
Commissioned Paper: An Overview of Pricing Models for Revenue Management
Manufacturing & Service Operations Management
Prediction, Learning, and Games
Prediction, Learning, and Games
A Nonparametric Approach to Multiproduct Pricing
Operations Research
An Analysis of the Control-Algorithm Re-solving Issue in Inventory and Revenue Management
Manufacturing & Service Operations Management
Relative Entropy, Exponential Utility, and Robust Dynamic Pricing
Operations Research
Toward Robust Revenue Management: Competitive Analysis of Online Booking
Operations Research
Dynamic Pricing for Nonperishable Products with Demand Learning
Operations Research
Dynamic Pricing with a Prior on Market Response
Operations Research
On the Minimax Complexity of Pricing in a Changing Environment
Operations Research
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We consider a general class of network revenue management problems, where mean demand at each point in time is determined by a vector of prices, and the objective is to dynamically adjust these prices so as to maximize expected revenues over a finite sales horizon. A salient feature of our problem is that the decision maker can only observe realized demand over time but does not know the underlying demand function that maps prices into instantaneous demand rate. We introduce a family of “blind” pricing policies that are designed to balance trade-offs between exploration demand learning and exploitation pricing to optimize revenues. We derive bounds on the revenue loss incurred by said policies in comparison to the optimal dynamic pricing policy that knows the demand function a priori, and we prove that asymptotically, as the volume of sales increases, this gap shrinks to zero.