Data-driven decision-making has garnered growing interest as a result of the increasing
availability of data in recent years. With that growth many opportunities and challenges have …

Benders decomposition uses a strategy of``learning from one's mistakes.''The aim of this
paper is to extend this strategy to a much larger class of problems. The key is to generalize …

With the rapid growth of the mobility-on-demand (MoD) market in recent years, ride-hailing
companies have become an important element of the urban mobility system. There are two …

Dynamic operating envelopes (DOEs) have been introduced in recent years as a means to
manage the operation of distributed energy resources (DERs) within the network operational …

We propose an approach based on machine learning to solve two-stage linear adaptive
robust optimization (ARO) problems with binary here-and-now variables and polyhedral …

When there are significant service disruptions in public transit systems, passengers usually
need guidance to find alternative paths. This paper proposes a path recommendation model …

Until now, the biofuel supply chain has lacked an integrated approach to address its
fundamental challenges in real-world implementation. The first challenge is maintaining a …

This paper addresses problems of two-stage optimization under binary uncertainty. We
define a scenario-based robust optimization (ScRO) formulation that combines principles of …

Energy forecasting models deployed in industrial applications face uncertainty wrt data
availability, due to network latency, equipment malfunctions or data-integrity attacks. In …

We consider the estimation of average treatment effects in observational studies and
propose a new framework of robust causal inference with unobserved confounders. Our …