Loubna Benabbou

Affiliate Member

UQAR

Research Topics

Deep Learning

Machine Learning Theory

Optimization

Website

Google Scholar

Current Students

Mohamed Yebari

Collaborating researcher - Ecole Polytechnique Montréal Fédérale de Lausanne (EPFL)

Principal supervisor :

David Rolnick

Publications

Learning Implicit Feasibility Constraints for Real-World Routing and Scheduling: Application to Log Transportation

Abdelhakim Abdellaoui

Ayoub Boufous

Issmail El Hallaoui

Loubna Benabbou

François Aubé

Mouloud Amazouz

Real-world vehicle routing and scheduling problems involve complex operational rules and feasibility constraints typically formulated as mix… (see more)ed-integer linear programs (MILP). However, optimization tools are built around a fixed set of hard-coded constraints, while in practice this set evolves as new rules or preferences emerge, seasonally or permanently. Updating it requires modeling and operations research skills that planners rarely have, so generated plans are routinely adjusted by hand based on practical knowledge. Building on recent work that uses machine learning to recover such hidden constraints, we propose a data-driven constraint-learning approach that trains three complementary predictors, a Graph Neural Network (GNN), a decision tree, and a linear regression, on historical execution data from a log-truck routing and scheduling problem (

2026-06-02

arXiv (preprint)

doi.org

arxiv.org

Multi-Representation Attention Framework for Underwater Bioacoustic Denoising and Recognition

Amine RAZIG

Youssef Soulaymani

Loubna Benabbou

Pierre Cauchy

2025-10-28

ArXiv (preprint)

doi.org

arxiv.org

Toward the Decarbonization of Maritime Supply Chains: A Ship Emissions Prediction Framework

Abdelhak El Aissi

Ismail Bourzak

Loubna Benabbou

Abdelaziz Berrado

Maritime transport is a vital component of international trade, yet the industry contributes substantially to greenhouse gas (GHG) emissions… (see more), with carbon dioxide

2025-10-19

2025 International Conference on Intelligent Systems: Theories and Applications (SITA) (published)

doi.org

Multiscale Neural PDE Surrogates for Prediction and Downscaling: Application to Ocean Currents

Abdessamad El-Kabid

Loubna Benabbou

Redouane Lguensat

Alex Hernández-García

Accurate modeling of physical systems governed by partial differential equations is a central challenge in scientific computing. In oceanogr… (see more)aphy, high-resolution current data are critical for coastal management, environmental monitoring, and maritime safety. However, available satellite products, such as Copernicus data for sea water velocity at ~0.08 degrees spatial resolution and global ocean models, often lack the spatial granularity required for detailed local analyses. In this work, we (a) introduce a supervised deep learning framework based on neural operators for solving PDEs and providing arbitrary resolution solutions, and (b) propose downscaling models with an application to Copernicus ocean current data. Additionally, our method can model surrogate PDEs and predict solutions at arbitrary resolution, regardless of the input resolution. We evaluated our model on real-world Copernicus ocean current data and synthetic Navier-Stokes simulation datasets.

2025-07-23

ArXiv (preprint)

doi.org

arxiv.org

Advancing Sustainable Maritime Transport: A Machine Learning Approach to Predict and Mitigate Underwater Radiated Noise from Ships

Soukaina Boujdi

Ayoub Atanane

Loubna Benabbou

Pierre Cauchy

2025-04-07

Proceedings of the International Conference on Industrial Engineering and Operations Management (published)

doi.org