Margarida Carvalho

Francesco Morri

Postdoctorat - Polytechnique

Co-superviseur⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Nagisa Sugishita

Postdoctorat - HEC

Publications

Integer Programming Games: A Gentle Computational Overview

Gabriele Dragotto

Andrea Lodi

Sriram Sankaranarayan

2024-12-31

Foundations and Trends® in Optimization (publié)

A Learning-Based Framework for Fair and Scalable Solution Generation in Kidney Exchange Problems

William St-Arnaud

2024-12-31

Trans. Mach. Learn. Res. (publié)

openreview.net

Software and Data for "Capacity Planning in Stable Matching"

Federico Bobbio

Andrea Lodi

Ignacio Rios

Alfredo Torrico

2024-12-31

Operational Research (publié)

A stochastic integer programming approach to reserve staff scheduling with preferences

Carl Perreault‐Lafleur

Guy Desaulniers

2024-12-31

Int. Trans. Oper. Res. (publié)

The Strength of Fuel Refueling Location Problem Formulations

Nagisa Sugishita

Ribal Atallah

2024-09-05

ArXiv (prépublication)

Accelerated Benders Decomposition and Local Branching for Dynamic Maximum Covering Location Problems

Steven Lamontagne

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.

2024-06-30

Computers & Operations Research (publié)

Maximum flow-based formulation for the optimal location of electric vehicle charging stations

Pierre‐Luc Parent

Miguel F. Anjos

Ribal Atallah

With the increasing effects of climate change, the urgency to step away from fossil fuels is greater than ever before. Electric vehicles (EV… (voir plus)s) are one way to diminish these effects, but their widespread adoption is often limited by the insufficient availability of charging stations. In this work, our goal is to expand the infrastructure of EV charging stations, in order to provide a better quality of service in terms of user satisfaction (and availability of charging stations). Specifically, our focus is directed towards urban areas. We first propose a model for the assignment of EV charging demand to stations, framing it as a maximum flow problem. This model is the basis for the evaluation of user satisfaction with a given charging infrastructure. Secondly, we incorporate the maximum flow model into a mixed‐integer linear program, where decisions on the opening of new stations and on the expansion of their capacity through additional outlets is accounted for. We showcase our methodology for the city of Montreal, demonstrating the scalability of our approach to handle real‐world scenarios. We conclude that considering both spacial and temporal variations in charging demand is meaningful when solving realistic instances.

2024-04-10

Networks (publié)

Learning to Build Solutions in Stochastic Matching Problems Using Flows (Student Abstract)

William St-Arnaud

Generative Flow Networks, known as GFlowNets, have been introduced in recent times, presenting an exciting possibility for neural networks t… (voir plus)o model distributions across various data structures. In this paper, we broaden their applicability to encompass scenarios where the data structures are optimal solutions of a combinatorial problem. Concretely, we propose the use of GFlowNets to learn the distribution of optimal solutions for kidney exchange problems (KEPs), a generalized form of matching problems involving cycles.

2024-03-23

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

The effects of nature-based vs. indoor settings on the adaptability, performance and affect of calisthenics exercisers. A registered report.

Henrique Brito

Henrique Lopes

Daniel Carrilho

Adriano Carvalho

Duarte Araújo

2024-02-29

Psychology of Sport And Exercise (publié)

The Sample Average Approximation Method for Solving Two-Stage Stochastic Programs with Endogenous Uncertainty

Maria Bazotte

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.

2024-02-22

ArXiv (prépublication)

Diagnosis Model for Detection of e-threats Against Soft-Targets

Sónia M. A. Morgado

Sérgio Felgueiras

2024-02-13

Lecture Notes in Networks and Systems (publié)

Asymmetry in the complexity of the multi-commodity network pricing problem

Quang Minh Bui

José Neto

2023-12-31

Math. Program. (publié)