Portrait of Emma Frejinger

Emma Frejinger

Associate Academic Member
Associate Professor, Université de Montréal, Department of Computer Science and Operations Research Department
Research Topics
Optimization
Reinforcement Learning

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
ben.hudson@mila.quebec
Website
Github
Google Scholar

Publications

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
A Capacitated Collection-and-Delivery-Point Location Problem with Random Utility Maximizing Customers
David Pinzon Ulloa
Ammar Metnan
Emma Frejinger
2024-11-06
ArXiv (preprint)
arxiv.org
arxiv.org
A model-free approach for solving choice-based competitive facility location problems using simulation and submodularity
Robin Legault
Emma Frejinger
This paper considers facility location problems in which a firm entering a market seeks to open facilities on a subset of candidate location… (see more)s so as to maximize its expected market share, assuming that customers choose the available alternative that maximizes a random utility function. We introduce a deterministic equivalent reformulation of this stochastic problem as a maximum covering location problem with an exponential number of demand points, each of which is covered by a different set of candidate locations. Estimating the prevalence of these preference profiles through simulation generalizes a sample average approximation method from the literature and results in a maximum covering location problem of manageable size. To solve it, we develop a partial Benders reformulation in which the contribution to the objective of the least influential preference profiles is aggregated and bounded by submodular cuts. This set of profiles is selected by a knee detection method that seeks to identify the best tradeoff between the fraction of the demand that is retained in the master problem and the size of the model. We develop a theoretical analysis of our approach and show that the solution quality it provides for the original stochastic problem, its computational performance, and the automatic profile-retention strategy it exploits are directly connected to the entropy of the preference profiles in the population. Computational experiments indicate that our approach dominates the classical sample average approximation method on large instances, can outperform the best heuristic method from the literature under the multinomial logit model, and achieves state-of-the-art results under the mixed multinomial logit model. We characterize a broader class of problems, which includes assortment optimization, to which the solving methodology and the analyses developed in this paper can be extended.
2024-07-15
INFORMS Journal on Computing (published)
doi.org
arxiv.org
A logistics provider’s profit maximization facility location problem with random utility maximizing followers
David Pinzon Ulloa
Emma Frejinger
Bernard Gendron
2024-07-01
Computers & Operations Research (published)
doi.org
arxiv.org
One-shot Learning for MIPs with SOS1 Constraints
Charly Robinson La Rocca
Jean-François Cordeau
Emma Frejinger
2024-07-01
Operations Research Forum (published)
doi.org
arxiv.org
Reinforcement learning for freight booking control problems
Justin Dumouchelle
Emma Frejinger
Andrea Lodi
2024-03-16
Journal of Revenue and Pricing Management (published)
doi.org
arxiv.org
Decoupling regularization from the action space
Sobhan Mohammadpour
Emma Frejinger
Pierre-Luc Bacon
Regularized reinforcement learning (RL), particularly the entropy-regularized kind, has gained traction in optimal control and inverse RL. W… (see more)hile standard unregularized RL methods remain unaffected by changes in the number of actions, we show that it can severely impact their regularized counterparts. This paper demonstrates the importance of decoupling the regularizer from the action space: that is, to maintain a consistent level of regularization regardless of how many actions are involved to avoid over-regularization. Whereas the problem can be avoided by introducing a task-specific temperature parameter, it is often undesirable and cannot solve the problem when action spaces are state-dependent. In the state-dependent action context, different states with varying action spaces are regularized inconsistently. We introduce two solutions: a static temperature selection approach and a dynamic counterpart, universally applicable where this problem arises. Implementing these changes improves performance on the DeepMind control suite in static and dynamic temperature regimes and a biological design task.
2024-01-16
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
Decoupling regularization from the action space
Sobhan Mohammadpour
Emma Frejinger
Pierre-Luc Bacon
Regularized reinforcement learning (RL), particularly the entropy-regularized kind, has gained traction in optimal control and inverse RL. W… (see more)hile standard unregularized RL methods remain unaffected by changes in the number of actions, we show that it can severely impact their regularized counterparts. This paper demonstrates the importance of decoupling the regularizer from the action space: that is, to maintain a consistent level of regularization regardless of how many actions are involved to avoid over-regularization. Whereas the problem can be avoided by introducing a task-specific temperature parameter, it is often undesirable and cannot solve the problem when action spaces are state-dependent. In the state-dependent action context, different states with varying action spaces are regularized inconsistently. We introduce two solutions: a static temperature selection approach and a dynamic counterpart, universally applicable where this problem arises. Implementing these changes improves performance on the DeepMind control suite in static and dynamic temperature regimes and a biological design task.
2024-01-16
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Maximum entropy GFlowNets with soft Q-learning
Sobhan Mohammadpour
Emmanuel Bengio
Emma Frejinger
Pierre-Luc Bacon
2024-01-01
AISTATS (published)
doi.org
arxiv.org
Pseudo-random Instance Generators in C++ for Deterministic and Stochastic Multi-commodity Network Design Problems
Eric Larsen
Serge Bisaillon
Jean-François Cordeau
Emma Frejinger
Network design problems constitute an important family of combinatorial optimization problems for which numerous exact and heuristic algorit… (see more)hms have been developed over the last few decades. Two central problems in this family are the multi-commodity, capacitated, fixed charge network design problem (MCFNDP) and its stochastic counterpart, the two-stage MCFNDP with recourse. These are standard problems that often serve as work benches for devising and testing models and algorithms in stylized but close-to-realistic settings. The purpose of this paper is to introduce two flexible, high-speed generators capable of simulating a wide range of settings for both the deterministic and stochastic MCFNDPs. We hope that, by facilitating systematic experimentation with new and larger sets of instances, these generators will lead to a more thorough assessment of the performance achieved by exact and heuristic solution methods in both deterministic and stochastic settings. We also hope that making these generators available will promote the reproducibility and comparability of published research.
2023-12-17
ArXiv (preprint)
arxiv.org
arxiv.org
A Survey of Contextual Optimization Methods for Decision Making under Uncertainty
Utsav Sadana
Abhilash Reddy Chenreddy
Érick Delage
Alexandre Forel
Emma Frejinger
Thibaut Vidal
2023-06-17
ArXiv (preprint)
doi.org
arxiv.org
Scope Restriction for Scalable Real-Time Railway Rescheduling: An Exploratory Study
Erik L. Nygren
Christian Eichenberger
Emma Frejinger
With the aim to stimulate future research, we describe an exploratory study of a railway rescheduling problem. A widely used approach in pra… (see more)ctice and state of the art is to decompose these complex problems by geographical scope. Instead, we propose defining a core problem that restricts a rescheduling problem in response to a disturbance to only trains that need to be rescheduled, hence restricting the scope in both time and space. In this context, the difficulty resides in defining a scoper that can predict a subset of train services that will be affected by a given disturbance. We report preliminary results using the Flatland simulation environment that highlights the potential and challenges of this idea. We provide an extensible playground open-source implementation based on the Flatland railway environment and Answer-Set Programming.
2023-05-05
ArXiv (preprint)
doi.org
arxiv.org