178 citations · 1.1k across the 46 of their papers we have counts for
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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…