178 citations · 1.1k across the 46 of their papers we have counts for
52 papers
A Survey of Contextual Optimization Methods for Decision Making under Uncertainty
Utsav Sadana, Abhilash Chenreddy, Erick Delage +3
Recently there has been a surge of interest in operations research (OR) and the machine learning (ML) community in combining prediction algorithms and optimization techniques to so…
Recent Advances in Vehicle Routing with Stochastic Demands: Bayesian Learning for Correlated Demands and Elementary Branch-Price-and-Cut
Alexandre M. Florio, Michel Gendreau, Richard F. Hartl +2
We consider the vehicle routing problem with stochastic demands (VRPSD), a stochastic variant of the well-known VRP in which demands are only revealed upon arrival of the vehicle a…
Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control
Kai Jungel, Axel Parmentier, Maximilian Schiffer +1
Autonomous mobility-on-demand systems are a viable alternative to mitigate many transportation-related externalities in cities, such as rising vehicle volumes in urban areas and tr…
Regularization and Optimization in Model-Based Clustering
Raphael Araujo Sampaio, Joaquim Dias Garcia, Marcus Poggi +1
Due to their conceptual simplicity, k-means algorithm variants have been extensively used for unsupervised cluster analysis. However, one main shortcoming of these algorithms is th…
Explainable Data-Driven Optimization: From Context to Decision and Back Again
Alexandre Forel, Axel Parmentier, Thibaut Vidal
Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. While a vast body of work is…
Influence Optimization in Networks: New Formulations and Valid Inequalities
Vinicius Ferreira, Artur Pessoa, Thibaut Vidal
Influence propagation has been the subject of extensive study due to its important role in social networks, epidemiology, and many other areas. Understanding propagation mechanisms…