activity
20182026
most citedA Survey of Contextual Optimization Methods for Decision Making under Uncertainty

152 citations · 308 across the 30 of their papers we have counts for

collaborators

30 papers

math.OC2026

Empirical Comparison of Unified Benders Cuts for Multi-Commodity Fixed-Charge Network Design

Eric Larsen, Jean-François Cordeau, Antonio Frangioni +1

Among the many types of acceleration techniques designed to improve the performance of Benders decomposition, unified cut generation schemes have recently attracted a keen interest…

cs.LG2026

Contextual Preference Distribution Learning

Benjamin Hudson, Laurent Charlin, Emma Frejinger

Decision-making problems often feature uncertainty stemming from heterogeneous and context-dependent human preferences. To address this, we propose a sequential learning-and-optimi…

math.OC2025

The Intermodal Railroad Blocking and Railcar Fleet-Management Planning Problem

Julie Kienzle, Serge Bisaillon, Teodor Gabriel Crainic +1

Rail is a cost-effective and relatively low-emission mode for transporting intermodal containers over long distances. This paper addresses tactical planning of intermodal railroad…

math.OC2025

Relative Explanations for Contextual Problems with Endogenous Uncertainty: An Application to Competitive Facility Location

Jasone Ramírez-Ayerbe, Emma Frejinger

In this paper, we consider a contextual stochastic optimization problem in which unknown parameters follow distributions that depend on contextual covariates and decisions. The pro…

math.OC2025★ 6 cited

What makes a good public EV charging station? A revealed preference study

Steven Lamontagne, Margarida Carvalho, Emma Frejinger +1

To determine the optimal locations for electric vehicle charging stations, optimisation models need to predict which charging stations users will select. We estimate discrete choic…

math.OC2025

Supervised Large Neighbourhood Search for MIPs

Charly Robinson La Rocca, Jean-François Cordeau, Emma Frejinger

Large Neighbourhood Search (LNS) is a powerful heuristic framework for solving Mixed-Integer Programming (MIP) problems. However, designing effective variable selection strategies…