The topic of dynamic pricing and learning has received a considerable amount of attention
in recent years, from different scientific communities. We survey these literature streams: we …

We review the operations research/management science literature on data‐driven methods
in retail operations. This line of work has grown rapidly in recent years, thanks to the …

We combine ideas from machine learning (ML) and operations research and management
science (OR/MS) in developing a framework, along with specific methods, for using data to …

Consider the newsvendor model, but under the assumption that the underlying demand
distribution is not known as part of the input. Instead, the only information available is a …

We consider a single-period inventory problem with random demand with both directly
observable and unobservable features that impact the demand distribution. With the recent …

We consider a repeated newsvendor problem in which the decision maker (DM) does not
have access to the underlying demand distribution. The goal of this paper is to characterize …

We consider a firm (eg, retailer) selling a single nonperishable product over a finite-period
planning horizon. Demand in each period is stochastic and price sensitive, and unsatisfied …

We consider a periodic-review single-product inventory system with fixed cost under
censored demand. Under full demand distributional information, it is well known that the …

We study the classical newsvendor problem in which the decision maker must trade off
underage and overage costs. In contrast to the typical setting, we assume that the decision …

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 …