We consider joint learning and pricing in network revenue management (NRM) with multiple products, multiple resources with finite capacity, parametric demand model, and a continuum set of feasible price vectors. We study the setting with a general parametric demand model and the setting with a...

We study dynamic pricing over a finite number of periods in the presence of demand model ambiguity. Departing from the typical no-regret learning environment, where price changes are allowed at any time, pricing decisions are made at pre-specified points in time and each price can be applied to...

We consider the joint learning and optimization problem of cyclic pricing policies in the presence of patient customers. In our problem, some customers can be patient, so that they are willing to wait in the system for several periods to make a purchase until the price is lower than or equal to...

We consider a pricing problem for a set of products displayed on a list. We assume a general cascade click model, in which customers examine the products in a decreasing order of display, from the top to (potentially) the bottom of the list. At each step, customers can decide to either purchase...

We propose the first learning algorithm for single-product, periodic-review, backlogging inventory systems with random production capacity. Different than the existing literature on this class of problems, we assume that the firm has neither prior information about the demand distribution nor...

We consider the joint optimization of ordering and upgrading decisions in a dynamic multiproduct system over a finite time horizon of $T$ periods. Multiple classes of demand arrive in each period stochastically following stationary distributions. Demand can be satisfied with supply of the same...