Portrait of Emma Frejinger

Emma Frejinger

Associate Academic Member
Associate Professor, Université de Montréal, Department of Computer Science and Operations Research Department
Research Topics
AI and Sustainability
Applied Machine Learning
Combinatorial Optimization
Optimization
Reinforcement Learning
Website

Biography

Emma Frejinger is an associate professor in the Department of Computer Science and Operations Research at Université de Montréal (UdeM). She holds a Canada Research Chair and an industrial chair funded by the Canadian National Railway Company.

Her research is application-driven and focuses on using innovative combinations of methodologies from machine learning and operations research to solve large-scale decision-making problems. She has extensive experience working with industry, particularly within the transportation sector, where she has led collaborative research projects.

Since 2018, Frejinger has also worked as a scientific advisor for IVADO Labs, developing AI solutions for the supply chain industry. Before joining UdeM in 2013, she was on the faculty of the KTH Royal Institute of Technology in Sweden. She holds a PhD in mathematics from the Swiss Federal Institute of Technology (EPFL).

Current Students

Ben Hudson
PhD - Université de Montréal
Principal supervisor :
Laurent Charlin
Website
Github
Google Scholar

Publications

The Intermodal Railroad Blocking and Railcar Fleet-Management Planning Problem
Julie Kienzle
Serge Bisaillon
T. Crainic
Emma Frejinger
Rail is a cost-effective and relatively low-emission mode for transporting intermodal containers over long distances. This paper addresses t… (see more)actical planning of intermodal railroad operations by introducing a new problem that simultaneously considers three consolidation processes and the management of a heterogeneous railcar fleet. We model the problem with a scheduled service network design with resource management (SSND-RM) formulation, expressed as an integer linear program. While such formulations are challenging to solve at scale, we demonstrate that our problem can be tackled with a general-purpose solver when provided with high-quality warm-start solutions. To this end, we design a construction heuristic inspired by a relax-and-fix procedure. We evaluate the methodology on realistic, large-scale instances from our industrial partner, the Canadian National Railway Company: a North American Class I railroad. The computational experiments show that the proposed approach efficiently solves practically relevant instances, and that solutions to the SSND-RM formulation yield substantially more accurate capacity estimations compared to those obtained from simpler baseline models. Managerial insights from our study highlight that ignoring railcar fleet management or container loading constraints can lead to a severe underestimation of required capacity, which may result in costly operational inefficiencies. Furthermore, our results show that the use of multi-platform railcars improves overall capacity utilization and benefits the network, even if they can locally lead to less efficient loading as measured by terminal-level slot utilization performance indicators.
2025-10-23
ArXiv (preprint)
arxiv.org
arxiv.org
Relative Explanations for Contextual Problems with Endogenous Uncertainty: An Application to Competitive Facility Location
Jasone Ram'irez-Ayerbe
Emma Frejinger
2025-06-23
ArXiv (preprint)
arxiv.org
arxiv.org
Relative Explanations for Contextual Problems with Endogenous Uncertainty: An Application to Competitive Facility Location
Jasone Ram'irez-Ayerbe
Emma Frejinger
In this paper, we consider contextual stochastic optimization problems under endogenous uncertainty, where decisions affect the underlying d… (see more)istributions. To implement such decisions in practice, it is crucial to ensure that their outcomes are interpretable and trustworthy. To this end, we compute relative counterfactual explanations that provide practitioners with concrete changes in the contextual covariates required for a solution to satisfy specific constraints. Whereas relative explanations have been introduced in prior literature, to the best of our knowledge this is the first work focusing on problems with binary decision variables and endogenous uncertainty. We propose a methodology that uses the Wasserstein distance as a regularization term, which leads to a reduction in computation times compared to its unregularized counterpart. We illustrate the method using a choice-based competitive facility location problem and present numerical experiments that demonstrate its ability to efficiently compute sparse and interpretable explanations.
2025-06-23
ArXiv (preprint)
arxiv.org
arxiv.org
What makes a good public EV charging station? A revealed preference study
Steven Lamontagne
Margarida Carvalho
Emma Frejinger
Ribal Atallah
To determine the optimal locations for electric vehicle charging stations, optimisation models need to predict which charging stations users… (see more) will select. We estimate discrete choice models to predict the usage of charging stations using only readily available information for charging network operators. Our parameter values are estimated from a unique, revealed preferences dataset of charging sessions in Montreal, Quebec. We find that user distance to stations, proximity to home areas, and the number of outlets at each station are significant factors for predicting station usage. Additionally, amenities near charging stations have a neutral effect overall, with some users demonstrating strong preference or aversion for these locations. High variability among the preferences of users highlight the importance of models which incorporate panel effects. Moreover, integrating mixed logit models within the optimization of charging station network design yields high-quality solutions, even when evaluated under other model specifications.
2025-04-24
ArXiv (preprint)
arxiv.org
arxiv.org
What makes a good public EV charging station? A revealed preference study
Steven Lamontagne
Margarida Carvalho
Emma Frejinger
Ribal Atallah
2025-04-24
ArXiv (preprint)
arxiv.org
arxiv.org
Perspectives on optimizing transport systems with supply-dependent demand
Emma Frejinger
Mike Hewitt
2025-03-26
INFOR. Information systems and operational research (published)
doi.org
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 … (see more)effective variable selection strategies in LNS remains challenging, especially for diverse sets of problems. In this paper, we propose an approach that integrates Machine Learning (ML) within the destroy operator of LNS for MIPs with a focus on minimal offline training. We implement a modular LNS matheuristic as a test bench to compare different LNS heuristics, including our ML-enhanced LNS. Experimental results on the MIPLIB 2017 dataset demonstrate that the matheuristic can significantly improve the performance of state-of-the-art solvers like Gurobi and SCIP. We conduct analyses on noisy oracles to explore the impact of prediction accuracy on solution quality. Additionally, we develop techniques to enhance the ML model through loss adjustments and sampling routines. Our findings suggest that while random LNS remains competitive, our Supervised LNS (SLNS) outperforms other baselines and helps set the foundation for future research on ML for LNS methods that are both efficient and general.
2025-01-18
ArXiv (preprint)
arxiv.org
arxiv.org
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 … (see more)effective variable selection strategies in LNS remains challenging, especially for diverse sets of problems. In this paper, we propose an approach that integrates Machine Learning (ML) within the destroy operator of LNS for MIPs with a focus on minimal offline training. We implement a modular LNS matheuristic as a test bench to compare different LNS heuristics, including our ML-enhanced LNS. Experimental results on the MIPLIB 2017 dataset demonstrate that the matheuristic can significantly improve the performance of state-of-the-art solvers like Gurobi and SCIP. We conduct analyses on noisy oracles to explore the impact of prediction accuracy on solution quality. Additionally, we develop techniques to enhance the ML model through loss adjustments and sampling routines. Our findings suggest that while random LNS remains competitive, our Supervised LNS (SLNS) outperforms other baselines and helps set the foundation for future research on ML for LNS methods that are both efficient and general.
2025-01-18
ArXiv (preprint)
arxiv.org
arxiv.org
An identification of models to help in the design of national strategies and policies to reduce greenhouse gas emissions.
Danielle Maia de Souza
Radhwane Boukelouha
Emma Frejinger
Catherine Morency
Normand Mousseau
Martin Trépanier
2025-01-01
Transportation Research Procedia (published)
doi.org
An identification of models to help in the design of national strategies and policies to reduce greenhouse gas emissions.
Danielle Maia de Souza
Radhwane Boukelouha
Emma Frejinger
Catherine Morency
Normand Mousseau
Martin Trépanier
2025-01-01
Transportation Research Procedia (published)
doi.org
A Survey of Contextual Optimization Methods for Decision Making under Uncertainty
Utsav Sadana
Abhilash Reddy Chenreddy
Erick Delage
Alexandre Forel
Emma Frejinger
Thibaut Vidal
2025-01-01
European Journal of Operational Research (published)
doi.org
arxiv.org
Combining supervised learning and local search for the multicommodity capacitated fixed-charge network design problem
Charly Robinson La Rocca
Jean-François Cordeau
Emma Frejinger
The multicommodity capacitated fixed-charge network design problem has been extensively studied in the literature due to its wide range of a… (see more)pplications. Despite the fact that many sophisticated solution methods exist today, finding high-quality solutions to large-scale instances remains challenging. In this paper, we explore how a data-driven approach can help improve upon the state of the art. By leveraging machine learning models, we attempt to reveal patterns hidden in the data that might be difficult to capture with traditional optimization methods. For scalability, we propose a prediction method where the machine learning model is called at the level of each arc of the graph. We take advantage of off-the-shelf models trained via supervised learning to predict near-optimal solutions. Our experimental results include an algorithm design analysis that compares various integration strategies of predictions within local search algorithms. We benchmark the ML-based approach with respect to the state-of-the-art heuristic for this problem. The findings indicate that our method can outperform the leading heuristic on sets of instances sampled from a uniform distribution.
2024-12-01
Transportation Research Part E: Logistics and Transportation Review (published)
doi.org
arxiv.org