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 investigate the data-driven newsvendor problem when one has n observations of p
features related to the demand as well as historical demand data. Rather than a two-step …

In this review, we discuss the data-driven systems and their effects on the implementation of
the inventory theory. After overviewing the theory briefly, we group the data-driven …

We consider an assortment optimization problem where a retailer chooses an assortment of
products that maximizes the profit subject to a capacity constraint. The demand is …

Retailers that offer perishable items are required to make ordering decisions for hundreds of
products on a daily basis. This task is non-trivial because the risk of ordering too much or too …

Traditional stochastic inventory models assume full knowledge of the demand probability
distribution. However, in practice, it is often difficult to completely characterize the demand …

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 …

Fixed costs of ordering items or setting up a production process arise in many real‐life
scenarios. In their presence, the most widely used ordering policy in the stochastic inventory …

We study stochastic inventory planning with lost sales and instantaneous replenishment
where, contrary to the classical inventory theory, knowledge of the demand distribution is not …

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 …