Recherche
Contexte
Le transport des personnes et des marchandises est omniprésent et connaît une transformation qui aura un impact énorme sur la société, l'environnement et l'économie. La demande est le moteur de cette transformation en adoptant les nouveaux services de transport. Il en résulte des systèmes de transport intégrés (TS) dans lesquels les distinctions historiques entre les modes de transport privés et publics ainsi qu'entre les personnes et les marchandises sont floues. Nous développons des méthodologies pour prévoir la demande et optimiser l'offre de TS intégrés en tenant compte de la réponse de la demande. Pour atteindre cet objectif, nous développons (i) des algorithmes d'apprentissage statistique et (ii) des modèles d'optimisation et des méthodes de solutions.
Articles
Soumis, en révision, accepté et publié. Les étudiants et postdocs sont indentifiés d'une étoile.
Combining supervised learning and local search for the multicommodity capacitated fixed-charge network design problem
Charly Robinson La Rocca, Jean-François Cordeau et Emma Frejinger
Transportation Research Part E, volume 192, 2024.
DOI: 10.48550/arXiv.2409.05720
One-shot learning for MIPs with SOS1 constraints
Charly Robinson La Rocca, Jean-François Cordeau et Emma Frejinger
Operations Research Forum, 5(57), 2024.
Reinforcement learning for freight booking control problems
Justin Dumouchelle, Emma Frejinger et Andrea Lodi
Journal of Revenue and Pricing Management, 23: 318-345, 2024.
DOI: 10.1057/s41272-023-00459-1
A Survey of Contextual Optimization Methods for Decision-Making under Uncertainty
Erick Delage, Utsav Sadana, Abhilash Chenreddy, Alexandre Forel, Emma Frejinger et Thibaut Vidal
European Journal of Operational Research, 320(2): 271-448. 2024.
A model-free approach for solving choice-based competitive facility location problems using simulation and submodularity
Robin Legault et Emma Frejinger
INFORMS Journal on Computing, publié en ligne le 15 juillet, 2024.
Optimising Electric Vehicle Charging Station Placement using Advanced Discrete Choice Models
Steven Lamontagne, Margarida Carvalho, Emma Frejinger, Bernard Gendron, Miguel F. Anjos et Ribal Atallah.
INFORMS Journal on Computing, 35(5),1195–1213, 2023.
Fast Continuous and Integer L-shaped Heuristics Through Supervised Learning
Eric Larsen, Emma Frejinger, Bernard Gendron et Andrea Lodi.
INFORMS Journal on Computing, 36(1), 2023.
DOI: 10.1287/ijoc.2022.0175
A Logistics Provider's Profit Maximization Facility Location Problem with Random Utility Maximizing Followers
David Pinzon, Emma Frejinger and Bernard Gendron.
Computers & OR, (167) 106649, 2024.
DOI: 10.48550/arXiv.2303.06749
The Load Planning and Sequencing Problem for Double-Stack Trains
Mortiz Ruf, Jean-François Cordeau and Emma Frejinger
Journal of Rail Transport Planning & Management, 23:100337, 2022.
Undiscounted Recursive Path Choice Models: Convergence Properties and Algorithms
Tien Mai et Emma Frejinger
Transportation Science, 56(6):1469-1482, Mai 2022.
A Time-Space Formulation for the Locomotive Routing Problem at the Canadian National Railways
Pedro Miranda, Jean-François Cordeau et Emma Frejinger
Computers and Operations Research, 139: 105629, novembre 2021.
Routing Policy Choice Prediction in a Stochastic Network: Recursive Model and Solution Algorithm
Tien Mai, Xinlian Yu, Song Gao et Emma Frejinger
Transportation Research Part B, 151: 42-58, Juillet 2021.
DOI: 10.1016/j.trb.2021.06.016
Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information
*Eric Larsen, *Sébastien Lachapelle, Yoshua Bengio, Emma Frejinger, Simon Lacoste-Julien et Andrea Lodi
INFORMS Journal on Computing, 34(1):227-242, 2022.
The Locomotive Assignment Problem with Distributed Power at the Canadian National Railway Company
*Camilo Ortiz-Astorquiza, Jean-François Cordeau et Emma Frejinger
Transportation Science. 55(2): 275-552, 2021.
A Strategic Markovian Traffic Equilibrium Model for Capacitated Networks
*Maëlle Zimmermann, Emma Frejinger et Patrice Marcotte
Transportation Science. Volume: 55, Numéro: 3 (Mai-Juin 2021): 574-591.
DOI: 10.1287/trsc.2020.1033
Integrated Inbound Train Split and Load Planning in an Intermodal Railway Terminal
*Bruno Bruck, Jean-François Cordeau et Emma Frejinger
Transportation Research Part B, 145: 270-289, 2021.
Data-driven Optimization Model Customization
Michael Hewitt et Emma Frejinger
European Journal on Operations Research, 287(2): 438-451, 2020.
Route choice behavior and travel information in a congested network: Static and dynamic recursive models
*Giselle De Moraes Ramos, *Tien Mai, Winnie Daamen, Emma Frejinger et Serge Hoogendoorn
Transportation Research Part C: Emerging Technologies 114: 681-693, 2020.
A tutorial on recursive models for analyzing and predicting path choice behavior
*Maëlle Zimmermann et Emma Frejinger
EURO Journal on Transportation and Logistics, 9(2):100004, 2020.
An empirical study on aggregation of alternatives and its influence on prediction in car type choice models
*Shiva Habibi, Emma Frejinger et Markus Sundberg
Transportation 46(3): 563-582, 2019.
The Load Planning Problem for Double-Stack Intermodal Trains
*Serena Mantovani, *Gianluca Morganti, *Nitish Umang, Teodor Gabriel Crainic, Emma Frejinger et *Eric Larsen
European Journal of Operations Research 267(1): 107-119, 2018.
A decomposition method for estimating recursive logit based route choice models
*Tien Mai, Fabian Bastin et Emma Frejinger
EURO Journal on Transportation and Logistics 7(3):253-275, 2018.
DOI: 10.1016/j.ejtl.2020.100004
Capturing correlation with a mixed recursive logit model for activity-travel scheduling
*Maëlle Zimmermann, *Oscar Blom Västberg, Emma Frejinger et Anders Karlström
Transportation Research Part C: Emerging Technologies 93: 273-291, 2018.
A dynamic programming approach for quickly estimating large network-based MEV models
*Tien Mai, Emma Frejinger, Mogens Fosgerau et Fabian Bastin
Transportation Research Part B 98(1): 179-197, 2017.
Bike route choice modelling using GPS data without choice sets of paths
*Maëlle Zimmermann, *Tien Mai et Emma Frejinger
Transportation Research Part C 75(1): 183-196, 2017.
On the similarities between random regret minimization and mother logit : The case of recursive route choice models
*Tien Mai, Fabian Bastin et Emma Frejinger
Journal of Choice Modelling 23(1): 21-33, 2017.
A misspecification test for logit-based route choice models
*Tien Mai, Emma Frejinger et Fabian Bastin
Economics of Transportation. 4(4): 215-226, 2015.
A nested recursive logit model for route choice analysis
*Tien Mai, Mogens Fosgerau et Emma Frejinger
Transportation Research Part B 75(1): 100-112, 2015.
A link-based network route choice model with unrestricted choice set
Mogens Fosgerau, Emma Frejinger et Anders Karlstöm
Transportation Research Part B 56(1): 70-80, 2013.
Cognitive Cost in Route Choice with Real-Time Traffic Information: An Exploratory Analysis
Song Gao, Emma Frejinger et Moshe Ben-Akiva
Transportation Research Part A 45(9): 916-926, 2011.
Adaptive route choices in risky traffic networks: A prospect theory approach
Song Gao, Emma Frejinger et Moshe Ben-Akiva
Transportation Research Part C 18(5): 727-740, 2010.
Sampling of alternatives for route choice modeling
Emma Frejinger, Michel Bierlaire et Moshe Ben-Akiva
Transportation Research Part B 43(10): 984-994, 2009.
Adaptive Route Choice Models in Stochastic Time-Dependent networks
Song Gao, Emma Frejinger et Moshe Ben-Akiva
Transportation Research Record 2085: 136-143, 2008.
Route choice modeling with network-free data
Michel Bierlaire et Emma Frejinger
Transportation Research Part C 16(2): 187-198, 2008.
Capturing correlation with subnetworks in route choice models
Emma Frejinger et Michel Bierlaire
Transportation Research Part B 41(3): 363-378, 2007.
Conférences
A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs
Yoshua Bengio, Emma Frejinger, Andrea Lodi, *Rahul Patel et *Sriram Sankaranarayanan
Seventeenth International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR). Acceptée pour publication, 2020.
Block planning for intermodal rail: methodology and case study
*Gianluca Morganti, Teodor Gabriel Crainic, Emma Frejinger et Nicoletta Ricciardi
Publiée au Transportation Research Procedia 47, 19–26, 2020.