This paper examines how data-driven personalized decisions can be made while preserving consumer privacy. Our setting is one in which the firm chooses a personalized price based on each new customer's vector of individual features; the true set of individual demand-generating parameters is...

This paper studies the classic price-based network revenue management (NRM) problem with demand learning. The retailer dynamically decides prices of n products over a finite selling season (of length T) subject to m resource constraints, with the purpose of maximizing the cumulative revenue. In...

This paper proposes a general framework/meta-policy to solve Revenue Management (RM) problems with demand learning and potentially large action space, constrained by initial unreplenishable resources. This framework combines the technique of primal-dual method in optimization and...

We consider a dynamic pricing problem in a system with reusable resources. Customers arrive randomly over time, according to a specified non-stationary rate, and each customer requests a service that uses a combination of different types of resources for a deterministic duration of time. The...