In this paper, we describe a novel iterative procedure called SISTA to learn the underlying cost in optimal transport problems. SISTA is a hybrid between two classical methods, coordinate descent ("S"-inkhorn) and proximal gradient descent ("ISTA"). It alternates between a phase of exact...

With rising customer expectations and increasing computational potential, many transportation services face real-time decision making in stochastic and dynamic environments. They often need to find and adapt complex plans that are effective now but also flexible with respect to future...

We consider a multi-cluster vehicle routing problem with pickups and deliveries. A cluster consists of a set of nodes with requests for shipment. A single depot serves all requests in each cluster by means of capacitated trucks which pick up a consignment and deliver the same at nodes with...

In most accounts of Artificial Intelligence (AI), humans are pitted against the machines, and concerns about relative physical and mental strength populate the discussion. There are, however, numerous processes and shifts that AI stimulates within the human context that are equally essential to...

Drawing upon recent contributions in the statistical literature, we present a new result on the convergence of recursive, stochastic algorithms which can be applied to economic models with learning. The formal result provides probability bounds for convergence which can be used to describe the...

We propose the first learning algorithm for single-product, periodic-review, backlogging inventory systems with random production capacity. Different than the existing literature on this class of problems, we assume that the firm has neither prior information about the demand distribution nor...

Artificial intelligence systems are increasingly demonstrating their capacity to make better predictions than human experts. Yet, recent studies suggest that professionals sometimes doubt the quality of these systems and overrule machine-based prescriptions. This paper explores the extent to...

The problem of efficient management of the information provided by many sources or equivalently of the aggregation of various models concerning a situation is considered. Such problems arise very often in decision making where the issue of modeling of the random factors introducing uncertainty...