Emma Frejinger

Associate Academic Member

Full Professor, Université de Montréal, Department of Computer Science and Operations Research

Research Topics

AI and Sustainability

Applied Machine Learning

Combinatorial Optimization

Optimization

Reinforcement Learning

Website

Google Scholar

Biography

Emma Frejinger is a 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 developing innovative combinations of machine learning and operations research methodologies to address large-scale decision-making problems. Emma has extensive experience collaborating with industry, particularly within the transportation sector.

Since 2018, she has also served as a scientific advisor for IVADO Labs, contributing to the development of AI solutions for the supply chain industry. In addition, she provides expert witness services and is an academic affiliate of Analysis Group. Before joining Université de Montréal in 2013, Emma was a faculty member at KTH Royal Institute of Technology in Sweden. She holds a Ph.D. in Mathematics from EPFL (Switzerland).

Current Students

Ben Hudson

PhD - Université de Montréal

Principal supervisor :

Publications

Block planning for intermodal rail: Methodology and case study

Gianluca Morganti

T. Crainic

Emma Frejinger

Nicolettta Ricciardi

2019-12-31

Transportation Research Procedia (published)

doi.org

Electric Vehicles Equilibrium Model that Considers Queue Delay and Mixed Traffic

Nurit Oliker

Miguel F. Anjos

Margarida Carvalho

Emma Frejinger

Bernard Gendron

This study develops an equilibrium model for electric vehicles (EVs) that considers both queue delays in charging stations and flow dependen… (see more)t travel times. This is a user equilibrium model that accounts for travel, charging and queuing time in the path choice modelling of EVs and the complementary traffic. Waiting and service times in charging stations are represented by an m/m/k queuing system. The model considers multiple vehicle and driver classes, expressing different battery capacity, initial charge state and range anxiety level. Feasible paths are found for multiple classes given their limited travel range. A numerical application exemplifies the limitations of EVs assignment and their impact on flow distribution.

2019-12-31

(published)

www.semanticscholar.org

Modeling Route Choice with Real-Time Information: Comparing the Recursive and Non-Recursive Approaches

Xinlian Yu

Tien Mai

Jing Ding-Mastera

Song Gao

Emma Frejinger

Transportation systems are inherently uncertain due to disruptions such as bad weather, incident and the randomness of traveler’s choices.… (see more) Real-time information allows travelers to adapt to actual traffic conditions and potentially mitigate the adverse effect of uncertainty. We study the routing policy choice problems in a stochastic time-dependent (STD) network. A routing policy is defined as a decision rule applied at the end of each link that maps the realized traffic condition to the decision on the link to take next. Two types of routing policy choice models are formulated with perfect online information (POI): recursive logit model and non-recursive logit model. In the non-recursive model, a choice set of routing policies between an origin-destination (OD) pair is generated, and a probabilistic choice is modeled at the origin, while the choice of the next link at each link is a deterministic execution of the chosen routing policy. In the recursive model, the probabilistic choice of the next link is modeled at each link, following the framework of dynamic discrete choice models. The difference between the two models results from the interplay of two sources of stochasticity, i.e., nature’s probability and choice probability. The two models are equivalent when either source of stochasticity is removed, that is, in a deterministic network (as shown in Fosgerau et al., 2013) or with deterministic choice. We use an illustrative example to explore the difference between the two models when both sources of stochasticity exist, and find that when a route has state-wise stochastic dominance over the other, the recursive model predicts more extreme choice probabilities. The relation can go either way when the two routes are non-dominated. We further compare the two models in terms of computational efficiency in estimation and prediction, and flexibility in systematic utility specification and modeling correlation.

2019-12-31

(published)

www.semanticscholar.org

A language processing algorithm for predicting tactical solutions to an operational planning problem under uncertainty

Emma Frejinger

Eric Larsen

This paper is devoted to the prediction of solutions to a stochastic discrete optimization problem. Through an application, we illustrate ho… (see more)w we can use a state-of-the-art neural machine translation (NMT) algorithm to predict the solutions by defining appropriate vocabularies, syntaxes and constraints. We attend to applications where the predictions need to be computed in very short computing time -- in the order of milliseconds or less. The results show that with minimal adaptations to the model architecture and hyperparameter tuning, the NMT algorithm can produce accurate solutions within the computing time budget. While these predictions are slightly less accurate than approximate stochastic programming solutions (sample average approximation), they can be computed faster and with less variability.

2019-10-17

ArXiv (preprint)

arxiv.org