The selection of products and prices offered by a firm significantly impacts its profits. Existing approaches do not provide flexible models that capture the joint effect of assortment and price. We propose a nonparametric framework in which each customer is represented by a particular price...

Optimization applications often depend upon a huge number of uncertain parameters. In many contexts, however, the amount of relevant data per parameter is small, and hence, we may have only imprecise estimates. We term this setting -- where the number of uncertainties is large, but all estimates...

Motivated by the availability of real-time data on customer characteristics, we consider the problem of personalizing the assortment of products for each arriving customer. Using actual sales data from an online retailer, we demonstrate that personalization based on each customer's location can...

Motivated by retailers' frequent introduction of new items to refresh product lines and maintain their market share, we present the assortment packing problem in which a firm must decide, in advance, the release date of each product in a given collection over a selling season. Our formulation...

Customer preferences may not be rational, so we focus on quantifying the limit of rationality (LoR) in choice modeling applications. We define LoR as the cost of approximating the observed choice fractions from a collection of offer sets with those from the best-fitting probability distribution...

Motivated by the poor performance of cross-validation in settings where data are scarce, we propose a novel estimator of the out-of-sample performance of a policy in data-driven optimization. Our approach exploits the optimization problem's sensitivity analysis to estimate the gradient of the...

We consider uncapacitated and capacitated assortment problems under the paired combinatorial logit model, where the goal is to fi nd a set of products to maximize the expected revenue obtained from each customer. In the uncapacitated setting, we can offer any set of products, whereas in the...

To provide the most protection, for the most people, in the least amount of time during a pandemic, an efficient and effective system is needed for allocating and distributing vaccines. By doing so, the risk and severity of illness and the impact of virus variants can be reduced. Short response...

We consider a multiarmed bandit problem where the expected reward of each arm is a linear function of an unknown scalar with a prior distribution. The objective is to choose a sequence of arms that maximizes the expected total (or discounted total) reward. We demonstrate the effectiveness of a...

Revenue management decisions often have both strategic and tactical components. Strategic decisions happen first and set the broad and long-term operational context in which tactical decisions are frequently and repeatedly made, often on a weekly or daily basis. We consider a joint optimization...