Many problems in artificial intelligence require adaptively making a sequence of decisions
with uncertain outcomes under partial observability. Solving such stochastic optimization …

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 …

This work examines the impact of discharge decisions under uncertainty in a capacity-
constrained high-risk setting: the intensive care unit (ICU). New arrivals to an ICU are …

Motivated by online labor markets, we consider the online assortment optimization problem
faced by a two-sided matching platform that hosts a set of suppliers waiting to match with a …

Motivated by the applications of rental services in e-commerce, we consider revenue
maximization in online assortment of reusable resources for a stream of arriving consumers …

We consider an online assortment optimization problem where we have n substitutable
products with fixed reusable capacities c 1,…, cn. In each period t, a user with some …

Motivated by the dynamic assortment offerings and item pricings occurring in e-commerce,
we study a general problem of allocating finite inventories to heterogeneous customers …

Motivated by applications of rental services in e-commerce, we consider real-time
assortment of reusable products. In our model, arriving consumers with heterogeneous …

We study the problem of maximizing a stochastic monotone submodular function with
respect to a matroid constraint. Because of the presence of diminishing marginal values in …

In this paper, we introduce and study the joint inventory selection and online resource
allocation problem, which is characterized by two sequential sets of decisions that are …