Portrait de Andrea Lodi

Andrea Lodi

Membre académique associé
Professeur associé, Polytechnique Montréal, Département de mathématiques et de génie industriel (MAGI)
Fondateur et directeur scientifique, IVADO Labs
Sujets de recherche
Optimisation
Google Scholar

Biographie

Andrea Lodi est professeur associé au Département de mathématiques et de génie industriel de Polytechnique Montréal. Il est aussi le fondateur et directeur scientifique d’IVADO Labs.

Depuis 2014, il est titulaire de la Chaire d'excellence en recherche du Canada sur la science des données pour la prise de décision en temps réel (Polytechnique Montréal), la chaire de recherche la plus importante au pays dans le domaine de la recherche opérationnelle. Reconnu internationalement pour ses travaux sur la programmation mixte linéaire et non linéaire, le professeur Lodi se concentre sur le développement de nouveaux modèles et algorithmes permettant de traiter rapidement et efficacement de vastes quantités de données de multiples sources. Ces algorithmes et modèles devraient conduire à la création de stratégies optimisées de prise de décision en temps réel. La Chaire a pour objectif d’appliquer son expertise dans divers secteurs, notamment l’énergie, les transports, la santé, la production et la gestion de la chaîne logistique.

Titulaire d'un doctorat en ingénierie des systèmes (2000), Andrea Lodi a été professeur titulaire de recherche opérationnelle au Département de génie électrique, électronique et informationnel de l'Université de Bologne. Il coordonne des projets de recherche opérationnelle européens à grande échelle et travaille depuis 2006 comme consultant auprès de l'équipe de recherche et développement CPLEX chez IBM. Il a publié plus de 70 articles dans de grandes revues de programmation mathématique et a été éditeur associé au sein de plusieurs d’entre elles.

Le professeur Lodi a reçu le prix Google 2010 du corps professoral et le prix IBM 2011 du corps professoral. Il a en outre été membre du prestigieux programme Herman Goldstine du centre de recherche IBM Thomas J. Watson en 2005-2006.

Publications

Measures of balance in combinatorial optimization
Philippe Olivier
Andrea Lodi
Gilles Pesant
2021-06-25
4OR (publié)
doi.org
Measures of balance in combinatorial optimization
Philippe Olivier
Andrea Lodi
G. Pesant
2021-06-25
4OR (published)
doi.org
Assessing the Impact: Does an Improvement to a Revenue Management System Lead to an Improved Revenue?
Greta Laage
Emma Frejinger
Andrea Lodi
Guillaume Rabusseau
2021-01-13
ArXiv (prépublication)
arxiv.org
arxiv.org
Capacity Expansion in the College Admission Problem
Federico Bobbio
Margarida Carvalho
Andrea Lodi
Alfredo Torrico
2021-01-01
arXiv.org (prépublication)
dblp.uni-trier.de
Estimating the Impact of an Improvement to a Revenue Management System: An Airline Application
Greta Laage
Emma Frejinger
William Hamilton
Andrea Lodi
Guillaume Rabusseau
Airlines have been making use of highly complex Revenue Management Systems to maximize revenue for decades. Estimating the impact of changin… (voir plus)g one component of those systems on an important outcome such as revenue is crucial, yet very challenging. It is indeed the difference between the generated value and the value that would have been generated keeping business as usual, which is not observable. We provide a comprehensive overview of counterfactual prediction models and use them in an extensive computational study based on data from Air Canada to estimate such impact. We focus on predicting the counterfactual revenue and compare it to the observed revenue subject to the impact. Our microeconomic application and small expected treatment impact stand out from the usual synthetic control applications. We present accurate linear and deep-learning counterfactual prediction models which achieve respectively 1.1% and 1% of error and allow to estimate a simulated effect quite accurately.
2021-01-01
arXiv.org (prépublication)
dblp.uni-trier.de
Multilevel Approaches for the Critical Node Problem
Andrea Baggio
Margarida Carvalho
Andrea Lodi
Andrea Tramontani
2021-01-01
Operational Research (publié)
doi.org
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Maxime Gasse
Simon Bowly
Quentin Cappart
Jonas Charfreitag
Laurent Charlin
Didier Chételat
Antonia Chmiela
Justin Dumouchelle
Ambros Gleixner
Aleksandr Kazachkov
Elias Boutros Khalil
Paweł Lichocki
Andrea Lodi
Miles Lubin
Chris J. Maddison
Christopher Morris
D. Papageorgiou
Augustin Parjadis
Sebastian Pokutta
Antoine Prouvost … (voir 22 de plus)
Lara Scavuzzo
Giulia Zarpellon
Linxin Yangm
Sha Lai
Akang Wang
Xiaodong Luo
Xiang Zhou
Haohan Huang
Sheng Cheng Shao
Yuanming Zhu
Dong Dong Zhang
Tao Manh Quan
Zixuan Cao
Yang Xu
Zhewei Huang
Shuchang Zhou
C. Binbin
He Minggui
Haoren Ren Hao
Zhang Zhiyu
An Zhiwu
Mao Kun
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused … (voir plus)on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning as a new approach for solving combinatorial problems, either directly as solvers or by enhancing exact solvers. Based on this context, the ML4CO aims at improving state-of-the-art combinatorial optimization solvers by replacing key heuristic components. The competition featured three challenging tasks: finding the best feasible solution, producing the tightest optimality certificate, and giving an appropriate solver configuration. Three realistic datasets were considered: balanced item placement, workload apportionment, and maritime inventory routing. This last dataset was kept anonymous for the contestants.
2021-01-01
NeurIPS (Competition and Demos) (publié)
doi.org
arxiv.org
BDD-based optimization for the quadratic stable set problem
Jaime E. González
Andr'e Augusto Cire
Andrea Lodi
Louis-Martin Rousseau
2020-08-01
Discrete Optimization (publié)
doi.org
BDD-based optimization for the quadratic stable set problem
Jaime E. González
Andr'e Augusto Cire
Andrea Lodi
Louis-Martin Rousseau
2020-08-01
Discrete Optimization (published)
doi.org
Multi-agent Assortment Optimization in Sequential Matching Markets
Alfredo Torrico
Margarida Carvalho
Andrea Lodi
2020-06-08
ArXiv (prépublication)
arxiv.org
arxiv.org
Provable Guarantees for General Two-sided Sequential Matching Markets
Alfredo Torrico
Margarida Carvalho
Andrea Lodi
Two-sided markets have become increasingly more important during the last years, mostly because of their numerous applications in housing, l… (voir plus)abor and dating. Consumer-supplier matching platforms pose several technical challenges, specially due to the trade-off between recommending suitable suppliers to consumers and avoiding collisions among consumers' preferences. In this work, we study a general version of the two-sided sequential matching model introduced by Ashlagi et al. (2019). The setting is the following: we (the platform) offer a menu of suppliers to each consumer. Then, every consumer selects, simultaneously and independently, to match with a supplier or to remain unmatched. Suppliers observe the subset of consumers that selected them, and choose either to match a consumer or leave the system. Finally, a match takes place if both the consumer and the supplier sequentially select each other. Each agent's behavior is probabilistic and determined by a regular discrete choice model. Our objective is to choose an assortment family that maximizes the expected cardinality of the matching. Given the computational complexity of the problem, we show several provable guarantees for the general model, which in particular, significantly improve the approximation factors previously obtained.
2020-06-08
arXiv.org (prépublication)
dblp.uni-trier.de
On generalized surrogate duality in mixed-integer nonlinear programming
Benjamin Müller
Gonzalo Muñoz
Maxime Gasse
Ambros Gleixner
Andrea Lodi
Felipe Serrano
2020-04-14
Integer Programming and Combinatorial Optimization (publié)
doi.org
arxiv.org