Jia-Ning Long

Graph learning-assisted logic-based Benders decomposition for distributionally robust in-station train rescheduling under disruptions

A rolling-horizon distributionally robust model that updates predictions of the remaining closure duration as observations of disruption resolution become available is developed, which minimizes the worst-case expected total completion delay over an ambiguity set of scenario probabilities.