Emma Frejinger

According to our database, Emma Frejinger authored at least 24 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Maximum entropy GFlowNets with soft Q-learning.

Sobhan Mohammadpour, Emmanuel Bengio, Emma Frejinger, Pierre-Luc Bacon

CoRR, 2023

A Survey of Contextual Optimization Methods for Decision Making under Uncertainty.

Utsav Sadana, Abhilash Reddy Chenreddy, Erick Delage, Alexandre Forel, Emma Frejinger, Thibaut Vidal

CoRR, 2023

Scope Restriction for Scalable Real-Time Railway Rescheduling: An Exploratory Study.

Erik Nygren, Christian Eichenberger, Emma Frejinger

CoRR, 2023

2022

Data-Driven Combinatorial Optimisation (Dagstuhl Seminar 22431).

Emma Frejinger, Andrea Lodi, Michele Lombardi, Neil Yorke-Smith

Dagstuhl Reports, October, 2022

The load planning and sequencing problem for double-stack trains.

Moritz Ruf, Jean-François Cordeau, Emma Frejinger

J. Rail Transp. Plan. Manag., 2022

Predicting Tactical Solutions to Operational Planning Problems Under Imperfect Information.

Eric Larsen, Sébastien Lachapelle, Yoshua Bengio, Emma Frejinger, Simon Lacoste-Julien, Andrea Lodi

INFORMS J. Comput., 2022

Arc travel time and path choice model estimation subsumed.

Sobhan Mohammadpour, Emma Frejinger

CoRR, 2022

Fast Continuous and Integer L-shaped Heuristics Through Supervised Learning.

Eric Larsen, Emma Frejinger, Bernard Gendron, Andrea Lodi

CoRR, 2022

Minimizing Entropy to Discover Good Solutions to Recurrent Mixed Integer Programs.

Charly Robinson La Rocca, Emma Frejinger, Jean-François Cordeau

CoRR, 2022

A time-space formulation for the locomotive routing problem at the Canadian National Railways.

Pedro L. Miranda, Jean-François Cordeau, Emma Frejinger

Comput. Oper. Res., 2022

2021

A Strategic Markovian Traffic Equilibrium Model for Capacitated Networks.

Maëlle Zimmermann, Emma Frejinger, Patrice Marcotte

Transp. Sci., 2021

The Locomotive Assignment Problem with Distributed Power at the Canadian National Railway Company.

Camilo Ortiz-Astorquiza, Jean-François Cordeau, Emma Frejinger

Transp. Sci., 2021

A Two-step Heuristic for the Periodic Demand Estimation Problem.

Greta Laage, Emma Frejinger, Gilles Savard

CoRR, 2021

Periodic Freight Demand Forecasting for Large-scale Tactical Planning.

Greta Laage, Emma Frejinger, Gilles Savard

CoRR, 2021

Can Machine Learning Help in Solving Cargo Capacity Management Booking Control Problems?

Justin Dumouchelle, Emma Frejinger, Andrea Lodi

CoRR, 2021

Estimating the Impact of an Improvement to a Revenue Management System: An Airline Application.

Greta Laage, Emma Frejinger, William L. Hamilton, Andrea Lodi, Guillaume Rabusseau

CoRR, 2021

2020

Data-driven optimization model customization.

Mike Hewitt, Emma Frejinger

Eur. J. Oper. Res., 2020

A tutorial on recursive models for analyzing and predicting path choice behavior.

Maëlle Zimmermann, Emma Frejinger

EURO J. Transp. Logist., 2020

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

Xinlian Yu, Tien Mai, Jing Ding-Mastera, Song Gao, Emma Frejinger

CoRR, 2020

A Learning-Based Algorithm to Quickly Compute Good Primal Solutions for Stochastic Integer Programs.

Yoshua Bengio, Emma Frejinger, Andrea Lodi, Rahul Patel, Sriram Sankaranarayanan

Proceedings of the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, 2020

2019

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

Emma Frejinger, Eric Larsen

CoRR, 2019

2018

The load planning problem for double-stack intermodal trains.

Serena Mantovani, Gianluca Morganti, Nitish Umang, Teodor Gabriel Crainic, Emma Frejinger, Eric Larsen

Eur. J. Oper. Res., 2018

A decomposition method for estimating recursive logit based route choice models.

Tien Mai, Fabian Bastin, Emma Frejinger

EURO J. Transp. Logist., 2018

Predicting Solution Summaries to Integer Linear Programs under Imperfect Information with Machine Learning.

Eric Larsen, Sébastien Lachapelle, Yoshua Bengio, Emma Frejinger, Simon Lacoste-Julien, Andrea Lodi

CoRR, 2018