We study a single product pricing problem with demand censoring in an offline data-driven
setting. In this problem, a retailer has a finite amount of inventory and faces a random …

Traditional offline-learning studies in inventory control assume access to true demand
samples $\{d_n\} _ {n= 1}^{N} $. However, in many applications, true demand samples are …

A merchant dynamically sets prices in each time period when selling a product over a finite
time horizon with a given initial inventory. The merchant utilizes new external information …

We study a dynamic pricing problem with finite inventory and parametric uncertainty on the
demand distribution. Products are sold during selling seasons of finite length, and inventory …

A firm makes pricing and inventory replenishment decisions for a product over T periods to
maximize its expected total profit. Demand is random and price sensitive, and unsatisfied …

We consider a joint pricing and inventory control problem in which the customer's response
to selling price and the demand distribution are not known a priori. Unsatisfied demand is …

Decisions regarding new products are often difficult to make, and mistakes can have grave
consequences for a firm's bottom line. Often, firms lack important information about a new …

The gist of inventory management is balancing overage and underage costs, and the key to
striking that balance lies in understanding customer demand. If there is no uncertainty in …

We study a multi-period stochastic inventory system with backlogs. Demand in each period
is random and price sensitive, but the firm has little or no prior knowledge about the demand …

We consider an inventory control problem with lost‐sales in a shifting demand environment.
Over a planning horizon of T periods, demand distributions can change up to O (log T) times …