Stochastic programming involves large-scale optimization with exponentially many
scenarios. This paper proposes an optimization-based scenario reduction approach to …

In this paper, we model topology control through transmission switching as a recourse action
in the day-ahead operation of power systems with large-scale renewable generation …

Choice-based optimization problems are the family of optimization problems that incorporate
the stochasticity of individual preferences according to discrete choice models to make …

We present a method for bundling scenarios in a progressive hedging heuristic (PHH)
applied to stochastic service network design, where the uncertain demand is represented by …

Abstract Purpose of Review As weather-dependent renewable generation increases its
share in the generation mix of most electric energy systems, a stochastic unit commitment …

ABSTRACT Emergency Medical Services (EMS) managers are concerned with responding
to emergency calls in a timely manner. Redeployment and dispatching strategies can be …

Progressive Hedging (PH) is a well-known algorithm for solving multi-stage stochastic
convex optimization problems. Most previous extensions of PH for mixed-integer stochastic …

This paper studies the distributionally robust fair transit resource allocation model (DrFRAM)
under the Wasserstein ambiguity set to optimize the public transit resource allocation during …

Many dynamic decision problems involving uncertainty can be appropriately modeled as
multistage stochastic programs. However, most practical instances are so large and/or …

Multistage mixed-integer distributionally robust optimization (DRO) forms a class of
extremely challenging problems since their size grows exponentially with the number of …