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
William St-Arnaud
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Golnoosh Farnadi
Nagisa Sugishita
Postdoctorat - HEC

Publications

Integer Programming Games.
Margarida Carvalho
Gabriele Dragotto
Andrea Lodi
Sriram Sankaranarayanan 0002
2025-01-01
Foundations and Trends® in Optimization (publié)
doi.org
A Learning-Based Framework for Fair and Scalable Solution Generation in Kidney Exchange Problems
William St-Arnaud
Margarida Carvalho
Golnoosh Farnadi
2025-01-01
Trans. Mach. Learn. Res. (publié)
openreview.net
openreview.net
Software and Data for "Capacity Planning in Stable Matching"
Federico Bobbio
Margarida Carvalho
Andrea Lodi
Ignacio Rios
Alfredo Torrico
2025-01-01
Operational Research (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 Flow Refueling Location Problem Formulations and an Extension to Cyclic Routing
Nagisa Sugishita
Margarida Carvalho
Ribal Atallah
The Flow Refueling Location Problem (FRLP) is a stylized model for determining the optimal placement of refueling stations for vehicles with… (voir plus) limited travel ranges, such as hydrogen fuel cell vehicles and electric vehicles. A notable extension, the deviation FRLP, accounts for the possibility that drivers may deviate from their preferred routes to refuel or recharge. While solution techniques based on various mathematical programming formulations have been thoroughly explored for this extension, there is a lack of theoretical insights into the relationships and strengths of these formulations. In this work, for the deviation extension, we study two prominent FRLP formulations from the literature and compare their strengths in terms of linear programming (LP) relaxations. We show that the LP relaxation of one formulation yields a bound that is at least as tight as that of the other, which may explain its observed superior performance. Building on these insights, we address a common modeling assumption in the FRLP that requires drivers to use the same paths for their outbound and inbound trips. Specifically, we relax this assumption and introduce the cyclic FRLP, where drivers may use different paths in each direction. We show how existing formulations can be naturally extended to accommodate this setting and describe a branch-and-cut algorithm to solve the problem. We provide numerical experiments highlighting the benefits of such asymmetric routing. For example, in an instance based on the Californian network, the cyclic FRLP serves all demands using 30% fewer facilities than the original FRLP.
2024-09-06
ArXiv (prépublication)
arxiv.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
The Sample Average Approximation Method for Solving Two-Stage Stochastic Programs with Endogenous Uncertainty
Maria Bazotte
Margarida Carvalho
Thibaut Vidal
Real-world decision-making problems involve Type 1 decision-dependent uncertainty, where the probability distribution of the stochastic proc… (voir plus)ess depends on the model decisions. However, few studies focus on two-stage stochastic programs with this type of endogenous uncertainty, and those that do lack general methodologies. We thus propose herein a general method for solving a class of these programs based on the transformation of random variables, a technique widely employed in probability and statistics. The proposed method is tailored to large-scale problems with discrete or continuous endogenous random variables. The random variable transformation allows the use of the sample average approximation (SAA) method, which provides optimality convergence guarantees under certain conditions. We show that, for some classical distributions, the proposed method reduces to solving mixed-integer linear or convex programs. Finally, we validate this method by applying it to a network design and facility-protection problem, considering distinct decision-dependent distributions for the random variables. Whereas most distributions result in a nonlinear nonconvex deterministic equivalent program, the proposed method solves mixed-integer linear programs in all cases. In addition, it produces attractive performance estimators for the SAA method in a reasonable computational time and outperforms the case in which the endogenous distribution defines a mixed-integer deterministic equivalent.
2024-02-23
ArXiv (prépublication)
arxiv.org
arxiv.org