The operations management discipline has made substantial progress developing analytical models to help firms make tactical and strategic decisions (e.g., inventory control, assortment planning). The application of these models to real contexts requires several inputs, such as an estimate of the...

Understanding consumer preferences is critical when optimizing prices and planning in retail operations, and when matching supply and demand in online platforms. In pursuing such objective, the identification of the consideration set of the consumers (i.e., the set of products really accounted...

A technology firm launches newer generations of a given product over time. At any moment, the firm decides whether to release a new version of the product that captures the current technology level at the expense of a fixed launch cost. Consumers are forward-looking and purchase newer models...

We propose a back-to-back procedure for running personalized promotions in retail operations contexts, from the construction of a nonparametric choice model where customer preferences are represented by directed acyclic graphs (DAGs) to the design of such promotions. The source data includes a...

We consider a choice-based, network revenue management (RM) problem in a setting where heterogeneous customers consider an assortment of products offered by a firm (e.g., different flight times, fare classes, and/or routes). Individual choice decisions are modeled through an ordered list of...

We analyze a revenue management problem in which a seller facing a Poisson arrival stream of consumers operates an online multiunit auction. Consumers can get the product from an alternative list price channel. We consider two variants of this problem: In the first variant, the list price is an...