Portrait de Margarida Carvalho

Margarida Carvalho

Membre académique associé
Professeure adjointe, Université de Montréal, Département d'informatique et de recherche opérationnelle
Sujets de recherche
Équité algorithmique
IA et durabilité
Optimisation
Optimisation combinatoire
Théorie de la décision
Théorie des jeux
Site web
Google Scholar

Biographie

Margarida Carvalho est titulaire d'un baccalauréat et d'une maîtrise en mathématiques. Elle a obtenu un doctorat en informatique à l'Université de Porto, pour lequel elle a reçu le prix de la thèse EURO en 2018. La même année, elle est devenue professeure adjointe au Département d'informatique et de recherche opérationnelle de l'Université de Montréal, où elle occupe la Chaire de recherche FRQ-IVADO en science des données pour la théorie des jeux combinatoires.

Elle est une experte en recherche opérationnelle, notamment en optimisation combinatoire et en théorie algorithmique des jeux. Ses recherches sont motivées par des problèmes de prise de décision du monde réel impliquant l'interaction de plusieurs agents, tels que les programmes d'échange de reins, le choix des écoles et les marchés concurrentiels.

Étudiants actuels

Marylou Fauchard
Maîtrise recherche - UdeM
Co-superviseur⋅e :
Golnoosh Farnadi
marylou.fauchard@mila.quebec
William St-Arnaud
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Golnoosh Farnadi

Publications

Solving Combinatorial Pricing Problems using Embedded Dynamic Programming Models
Quang Minh Bui
Margarida Carvalho
José Neto
The combinatorial pricing problem (CPP) is a bilevel problem in which the leader maximizes their revenue by imposing tolls on certain items … (voir plus)that they can control. Based on the tolls set by the leader, the follower selects a subset of items corresponding to an optimal solution of a combinatorial optimization problem. To accomplish the leader's goal, the tolls need to be sufficiently low to discourage the follower from choosing the items offered by the competitors. In this paper, we derive a single-level reformulation for the CPP by rewriting the follower's problem as a longest path problem using a dynamic programming model, and then taking its dual and applying strong duality. We proceed to solve the reformulation in a dynamic fashion with a cutting plane method. We apply this methodology to 2 distinct dynamic programming models, namely, a novel formulation designated as selection diagram and the well-known decision diagram. We also produce numerical results to evaluate their performances across 3 different specializations of the CPP and a closely related problem that is the knapsack interdiction problem. Our results showcase the potential of the 2 proposed reformulations over the natural value function approach, expanding the set of tools to solve combinatorial bilevel programs.
2025-04-28
INFORMS Journal on Computing (publié)
doi.org
arxiv.org
A Learning-Based Framework for Fair and Scalable Solution Generation in Kidney Exchange Problems
William St-Arnaud
Margarida Carvalho
Golnoosh Farnadi
2025-04-27
TMLR (accepté)
openreview.net
openreview.net
What makes a good public EV charging station? A revealed preference study
Steven Lamontagne
Margarida Carvalho
Emma Frejinger
Ribal Atallah
2025-04-24
ArXiv (prépublication)
arxiv.org
arxiv.org
Integer Programming Games.
Margarida Carvalho
Gabriele Dragotto
Andrea Lodi 0001
Sriram Sankaranarayanan 0002
2025-01-01
Found. Trends Optim. (publié)
doi.org
Integer Programming Games.
Margarida Carvalho
Gabriele Dragotto
Andrea Lodi
Sriram Sankaranarayanan 0002
2025-01-01
Foundations and Trends® in Optimization (publié)
doi.org
A stochastic integer programming approach to reserve staff scheduling with preferences
Carl Perreault‐Lafleur
Margarida Carvalho
Guy Desaulniers
2025-01-01
Int. Trans. Oper. Res. (publié)
doi.org
arxiv.org
The Strength of Fuel Refueling Location Problem Formulations
Nagisa Sugishita
Margarida Carvalho
Ribal Atallah
2024-09-06
ArXiv (prépublication)
arxiv.org
arxiv.org
Accelerated Benders Decomposition and Local Branching for Dynamic Maximum Covering Location Problems
Steven Lamontagne
Margarida Carvalho
Ribal Atallah
The maximum covering location problem (MCLP) is a key problem in facility location, with many applications and variants. One such variant is… (voir plus) the dynamic (or multi-period) MCLP, which considers the installation of facilities across multiple time periods. To the best of our knowledge, no exact solution method has been proposed to tackle large-scale instances of this problem. To that end, in this work, we expand upon the current state-of-the-art branch-and-Benders-cut solution method in the static case, by exploring several acceleration techniques. Additionally, we propose a specialised local branching scheme, that uses a novel distance metric in its definition of subproblems and features a new method for efficient and exact solving of the subproblems. These methods are then compared through extensive computational experiments, highlighting the strengths of the proposed methodologies.
2024-07-01
Computers & Operations Research (publié)
doi.org
arxiv.org
Learning to Build Solutions in Stochastic Matching Problems Using Flows (Student Abstract)
William St-Arnaud
Margarida Carvalho
Golnoosh Farnadi
2024-03-24
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
The effects of nature-based vs. indoor settings on the adaptability, performance and affect of calisthenics exercisers. A registered report.
Henrique Brito
Henrique Lopes
Margarida Carvalho
Daniel Carrilho
Adriano Carvalho
Duarte Araújo
2024-03-01
Psychology of Sport And Exercise (publié)
doi.org
The effects of nature-based vs. indoor settings on the adaptability, performance and affect of calisthenics exercisers. A registered report.
Henrique Brito
Henrique Lopes
Margarida Carvalho
Daniel Carrilho
Adriano Carvalho
Duarte Araújo
2024-03-01
Psychology of Sport And Exercise (publié)
doi.org
Solving Two-Stage Stochastic Programs with Endogenous Uncertainty via Random Variable Transformation
Maria Bazotte
Margarida Carvalho
Thibaut Vidal
2024-02-23
ArXiv (prépublication)
arxiv.org
arxiv.org