Portrait of Andrea Lodi

Andrea Lodi

Associate Academic Member
Adjunct Professor, Polytechnique Montréal, Mathematics and Industrial Engineering Department
Founder and Scientific Director, IVADO Labs
Research Topics
Optimization
Google Scholar

Biography

Andrea Lodi is an adjunct professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal, and founder and scientific director of IVADO Labs.

Since 2014, Lodi has held the Canada Excellence Research Chair in Data Science for Real-Time Decision-Making at Polytechnique Montréal. This is Canada’s leading research chair in the field of operations research.

Internationally recognized for his work on mixed linear and nonlinear programming, Lodi is focused on developing new models and algorithms to quickly and efficiently process massive amounts of data from multiple sources. These algorithms and models are expected to lead to the creation of optimized real-time decision-making strategies. The goal of his work as Chair is to apply this expertise in a range of sectors, including energy, transport, health, production and supply chain logistics management.

Lodi holds a PhD in systems engineering (2000) and is a full professor of operations research in the Department of Electrical, Electronic and Information Engineering at the University of Bologna. He coordinates large-scale European operations research projects, and has worked as a consultant for the CPLEX R&D team at IBM since 2006. Lodi has published over seventy articles in major journals in mathematical programming and also served as an associate editor for many of these journals.

His many honours include a 2010 Google Faculty Research Award and a 2011 IBM Faculty Award, and he was a member of the prestigious Herman Goldstine program at the IBM Thomas J. Watson Research Center in 2005–2006.

Publications

The Critical Node Game
Gabriele Dragotto
Amine Boukhtouta
Andrea Lodi
Mehdi Taobane
Cloud networks are the backbone of the modern distributed internet infrastructure as they provision most of the on-demand resources organiza… (see more)tions and individuals use daily. However, any abrupt cyber-attack could disrupt the provisioning of some of the cloud resources fulfilling the needs of customers, industries, and governments. In this work, we introduce a game-theoretic model that assesses the cyber-security risk of cloud networks and informs security experts on the optimal security strategies. Our approach combines game theory, combinatorial optimization, and cyber-security and aims at minimizing the unexpected network disruptions caused by malicious cyber-attacks under uncertainty. Methodologically, our approach consists of a simultaneous and non-cooperative attacker-defender game where each player solves a combinatorial optimization problem parametrized in the variables of the other player. Practically, our approach enables security experts to (i.) assess the security posture of the cloud network, and (ii.) dynamically adapt the level of cyber-protection deployed on the network. We provide a detailed analysis of a real-world cloud network and demonstrate the efficacy of our approach through extensive computational tests.
2023-03-10
ArXiv (preprint)
doi.org
arxiv.org
A Convex Reformulation and an Outer Approximation for a Large Class of Binary Quadratic Programs
Borzou Rostami
Fausto Errico
Andrea Lodi
2023-03-01
Operations Research (published)
doi.org
Game theoretical analysis of Kidney Exchange Programs
Margarida Carvalho
Andrea Lodi
2023-02-01
European Journal of Operational Research (published)
doi.org
arxiv.org
Cutting Planes from the Branch-and-Bound Tree: Challenges and Opportunities
Claudio Contardo
Andrea Lodi
Andrea Tramontani
2023-01-01
INFORMS J. Comput. (published)
doi.org
Optimizing Fairness over Time with Homogeneous Workers (Short Paper).
Bart-jan Van Rossum
Rui Chen
Andrea Lodi
2023-01-01
ATMOS (published)
doi.org
Lookback for Learning to Branch
Prateek Gupta
Elias Boutros Khalil
Didier Chételat
Maxime Gasse
Yoshua Bengio
Andrea Lodi
M. Pawan Kumar
2022-10-10
TMLR (accepted)
doi.org
openreview.net
OptiMaP: swarm-powered Optimized 3D Mapping Pipeline for emergency response operations
Leandro R. Costa
Daniel Aloise
Luca G. Gianoli
Andrea Lodi
A smart application in sensing is mainly powered by a two-stage process comprising sensing (collect data) and computing (process data). Whil… (see more)e the sensing stage is typically performed locally through a dedicated Internet of Things infrastructure, the computing stage may require a powerful infrastructure in the cloud. However, when connectivity is poor and low latency becomes a requirement — as in emergency response and disaster relief operations — edge computing and ad hoc cloud paradigms come in support to keep the computing stage locally. Being local network connectivity and data processing limited, it is vital to properly optimize how the computing workload will be consumed by the local ad hoc cloud. For this purpose, we present and evaluate the swarm-powered Optimized 3D Mapping Pipeline (OptiMaP) for emergency response 3D mapping missions, which is implemented as a collaborative embedded Robot Operating System (ROS) application integrating an ad hoc telecommunication middleware.We simulate — with Software-In-The-Loop — realistic 3D mapping missions comprising up to 5 drones and 363 images covering 0.293km2. We show how the completion times of mapping missions carried out in a typical centralized manner can be dramatically reduced by two versions of the OptiMaP framework powered, respectively, by a variable neighborhood search heuristic and a greedy method.
2022-06-01
2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) (published)
doi.org
Capacity Variation in the Many-to-one Stable Matching
Federico Bobbio
Margarida Carvalho
Andrea Lodi
Alfredo Torrico
2022-05-03
ArXiv (preprint)
doi.org
arxiv.org
OptiMaP: swarm-powered Optimized 3D Mapping Pipeline for emergency response operations
Leandro Rincon Costa
Daniel Aloise
Luca Giovanni Gianoli
Andrea Lodi
A smart application in sensing is mainly powered by a two-stage process comprising sensing (collect data) and computing (process data). Whil… (see more)e the sensing stage is typically performed locally through a dedicated Internet of Things infrastructure, the computing stage may require a powerful infrastructure in the cloud. However, when connectivity is poor and low latency becomes a requirement — as in emergency response and disaster relief operations — edge computing and ad hoc cloud paradigms come in support to keep the computing stage locally. Being local network connectivity and data processing limited, it is vital to properly optimize how the computing workload will be consumed by the local ad hoc cloud. For this purpose, we present and evaluate the swarm-powered Optimized 3D Mapping Pipeline (OptiMaP) for emergency response 3D mapping missions, which is implemented as a collaborative embedded Robot Operating System (ROS) application integrating an ad hoc telecommunication middleware.We simulate — with Software-In-The-Loop — realistic 3D mapping missions comprising up to 5 drones and 363 images covering 0.293km2. We show how the completion times of mapping missions carried out in a typical centralized manner can be dramatically reduced by two versions of the OptiMaP framework powered, respectively, by a variable neighborhood search heuristic and a greedy method.
2022-05-01
International Conference on Distributed Computing in Sensor Systems (published)
doi.org
Predicting the probability distribution of bus travel time to measure the reliability of public transport services
L'ea Ricard
Guy Desaulniers
Andrea Lodi
Louis-Martin Rousseau
2022-05-01
Transportation Research Part C: Emerging Technologies (published)
doi.org
Predicting the probability distribution of bus travel time to measure the reliability of public transport services
L. Ricard
Guy Desaulniers
Andrea Lodi
Louis-Martin Rousseau
2022-05-01
Transportation Research Part C: Emerging Technologies (published)
doi.org
On the estimation of discrete choice models to capture irrational customer behaviors
Sanjay Dominik Jena
Andrea Lodi
Claudio Sole
The random utility maximization model is by far the most adopted framework to estimate consumer choice behavior. However, behavioral economi… (see more)cs has provided strong empirical evidence of irrational choice behaviors, such as halo effects, that are incompatible with this framework. Models belonging to the random utility maximization family may therefore not accurately capture such irrational behavior. Hence, more general choice models, overcoming such limitations, have been proposed. However, the flexibility of such models comes at the price of increased risk of overfitting. As such, estimating such models remains a challenge. In this work, we propose an estimation method for the recently proposed generalized stochastic preference choice model, which subsumes the family of random utility maximization models and is capable of capturing halo effects. In particular, we propose a column-generation method to gradually refine the discrete choice model based on partially ranked preference sequences. Extensive computational experiments indicate that our model, explicitly accounting for irrational preferences, can significantly boost the predictive accuracy on both synthetic and real-world data instances. Summary of Contribution: In this work, we propose an estimation method for the recently proposed generalized stochastic preference choice model, which subsumes the family of random utility maximization models and is capable of capturing halo effects. Specifically, we show how to use partially ranked preferences to efficiently model rational and irrational customer types from transaction data. Our estimation procedure is based on column generation, where relevant customer types are efficiently extracted by expanding a treelike data structure containing the customer behaviors. Furthermore, we propose a new dominance rule among customer types whose effect is to prioritize low orders of interactions among products. An extensive set of experiments assesses the predictive accuracy of the proposed approach by comparing it against rank-based methods with only rational preferences and with more general benchmarks from the literature. Our results show that accounting for irrational preferences can boost predictive accuracy by 12.5% on average when tested on a real-world data set from a large chain of grocery and drug stores.
2022-05-01
INFORMS Journal on Computing (published)
doi.org
arxiv.org