Portrait of Emma Frejinger

Emma Frejinger

Associate Academic Member
Associate Professor, Université de Montréal, Department of Computer Science and Operations Research Department

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

PhD - Université de Montréal
Principal supervisor :

Publications

A logistics provider’s profit maximization facility location problem with random utility maximizing followers
David Pinzon Ulloa
Bernard Gendron
Reinforcement learning for freight booking control problems
Justin Dumouchelle
One-shot Learning for MIPs with SOS1 Constraints
Charly Robinson La Rocca
Jean-François Cordeau
Decoupling regularization from the action space
Sobhan Mohammadpour
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.
Decoupling regularization from the action space
Sobhan Mohammadpour
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.
Maximum entropy GFlowNets with soft Q-learning
Sobhan Mohammadpour
Emmanuel Bengio
Pseudo-random Instance Generators in C++ for Deterministic and Stochastic Multi-commodity Network Design Problems
Eric P. Larsen
Serge Bisaillon
Jean-François Cordeau
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.
Optimising Electric Vehicle Charging Station Placement Using Advanced Discrete Choice Models
Steven Lamontagne
Bernard Gendron
Miguel F. Anjos
Ribal Atallah
D'epartement d'informatique et de recherche op'erationnelle
U. Montr'eal
S. O. Mathematics
U. Edinburgh
Institut de Recherche d'Hydro-Qu'ebec
We present a new model for finding the optimal placement of electric vehicle charging stations across a multiperiod time frame so as to maxi… (see more)mise electric vehicle adoption. Via the use of stochastic discrete choice models and user classes, this work allows for a granular modelling of user attributes and their preferences in regard to charging station characteristics. We adopt a simulation approach and precompute error terms for each option available to users for a given number of scenarios. This results in a bilevel optimisation model that is, however, intractable for all but the simplest instances. Our major contribution is a reformulation into a maximum covering model, which uses the precomputed error terms to calculate the users covered by each charging station. This allows solutions to be found more efficiently than for the bilevel formulation. The maximum covering formulation remains intractable in some instances, so we propose rolling horizon, greedy, and greedy randomised adaptive search procedure heuristics to obtain good-quality solutions more efficiently. Extensive computational results are provided, and they compare the maximum covering formulation with the current state of the art for both exact solutions and the heuristic methods. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This work was supported by Hydro-Québec and the Natural Sciences and Engineering Research Council of Canada [Discovery grant 2017-06054; Collaborative Research and Development Grant CRDPJ 536757–19]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0185 .
A Survey of Contextual Optimization Methods for Decision Making under Uncertainty
Utsav Sadana
Abhilash Reddy Chenreddy
Alexandre Forel
Thibaut Vidal
Scope Restriction for Scalable Real-Time Railway Rescheduling: An Exploratory Study
Erik L. Nygren
Christian Eichenberger
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.
The load planning and sequencing problem for double-stack trains
Moritz Ruf
Jean-François Cordeau
Assessing the Impact: Does an Improvement to a Revenue Management System Lead to an Improved Revenue?