249 citations · 790 across the 59 of their papers we have counts for
19 papers · 1 filter
A machine learning framework for neighbor generation in metaheuristic search
Defeng Liu, Vincent Perreault, Alain Hertz +1
This paper presents a methodology for integrating machine learning techniques into metaheuristics for solving combinatorial optimization problems. Namely, we propose a general mach…
A Framework for Fair Decision-making Over Time with Time-invariant Utilities
Andrea Lodi, Sriram Sankaranarayanan, Guanyi Wang
Fairness is a major concern in contemporary decision problems. In these situations, the objective is to maximize fairness while preserving the efficacy of the underlying decision-m…
Learning to repeatedly solve routing problems
Mouad Morabit, Guy Desaulniers, Andrea Lodi
In the last years, there has been a great interest in machine-learning-based heuristics for solving NP-hard combinatorial optimization problems. The developed methods have shown po…
Connectivity-constrained Interactive Panoptic Segmentation
Ruobing Shen, Bo Tang, Andrea Lodi +2
We address interactive panoptic annotation, where one segment all object and stuff regions in an image. We investigate two graph-based segmentation algorithms that both enforce con…
Online Decision Making with Fairness over Time
Rui Chen, Oktay Gunluk, Andrea Lodi +1
Online platforms increasingly rely on sequential decision-making algorithms to allocate resources, match users, or control exposure, while facing growing pressure to ensure fairnes…
Learning to Compare Nodes in Branch and Bound with Graph Neural Networks
Abdel Ghani Labassi, Didier Chételat, Andrea Lodi
Branch-and-bound approaches in integer programming require ordering portions of the space to explore next, a problem known as node comparison. We propose a new siamese graph neural…
Neural Networks for Local Search and Crossover in Vehicle Routing: A Possible Overkill?
Ítalo Santana, Andrea Lodi, Thibaut Vidal
Extensive research has been conducted, over recent years, on various ways of enhancing heuristic search for combinatorial optimization problems with machine learning algorithms. In…
Explainable prediction of Qcodes for NOTAMs using column generation
Krunal Kishor Patel, Guy Desaulniers, Andrea Lodi +1
A NOtice To AirMen (NOTAM) contains important flight route related information. To search and filter them, NOTAMs are grouped into categories called QCodes. In this paper, we devel…
Implementing a Hierarchical Deep Learning Approach for Simulating Multi-Level Auction Data
Igor Sadoune, Andrea Lodi, Marcelin Joanis
We present a deep learning solution to address the challenges of simulating realistic synthetic first-price sealed-bid auction data. The complexities encountered in this type of au…
Deep Neural Networks pruning via the Structured Perspective Regularization
Matteo Cacciola, Antonio Frangioni, Xinlin Li +1
In Machine Learning, Artificial Neural Networks (ANNs) are a very powerful tool, broadly used in many applications. Often, the selected (deep) architectures include many layers, an…
A Stochastic Proximal Method for Nonsmooth Regularized Finite Sum Optimization
Dounia Lakhmiri, Dominique Orban, Andrea Lodi
We consider the problem of training a deep neural network with nonsmooth regularization to retrieve a sparse and efficient sub-structure. Our regularizer is only assumed to be lowe…
Fast Continuous and Integer L-shaped Heuristics Through Supervised Learning
Eric Larsen, Emma Frejinger, Bernard Gendron +1
We propose a methodology at the nexus of operations research and machine learning (ML) leveraging generic approximators available from ML to accelerate the solution of mixed-intege…
Lookback for Learning to Branch
Prateek Gupta, Elias B. Khalil, Didier Chetélat +4
The expressive and computationally inexpensive bipartite Graph Neural Networks (GNN) have been shown to be an important component of deep learning based Mixed-Integer Linear Progra…
MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers
Elias B. Khalil, Christopher Morris, Andrea Lodi
Mixed-integer programming (MIP) technology offers a generic way of formulating and solving combinatorial optimization problems. While generally reliable, state-of-the-art MIP solve…
Capacity Variation in the Many-to-one Stable Matching
Federico Bobbio, Margarida Carvalho, Andrea Lodi +1
The many-to-one stable matching problem provides the fundamental abstraction of several real-world matching markets such as school choice and hospital-resident allocation. The agen…
Learning to branch with Tree MDPs
Lara Scavuzzo, Feng Yang Chen, Didier Chételat +4
State-of-the-art Mixed Integer Linear Program (MILP) solvers combine systematic tree search with a plethora of hard-coded heuristics, such as the branching rule. The idea of learni…
Continuous cutting plane algorithms in integer programming
Didier Chételat, Andrea Lodi
Cutting planes for mixed-integer linear programs (MILPs) are typically computed in rounds by iteratively solving optimization problems, the so-called separation. Instead, we refram…
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Maxime Gasse, Quentin Cappart, Jonas Charfreitag +38
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolatio…
Machine-learning-based arc selection for constrained shortest path problems in column generation
Mouad Morabit, Guy Desaulniers, Andrea Lodi
Column generation is an iterative method used to solve a variety of optimization problems. It decomposes the problem into two parts: a master problem, and one or more pricing probl…