arXiv subjects

Loubna Benabbou

Publications and source records attributed to Loubna Benabbou.

10 recordsLinked to original sources

arXiv2026

Learning to recover: Adaptive local branching with reinforcement learning for log-truck routing and scheduling under disruptions

We consider the real-time reoptimisation of log-truck routing and scheduling in the Canadian forestry industry following unforeseen disruptions. Road closures, vehicle breakdowns, travel delays, and demand fluctuations invalidate pre-established tactical plans and call for recovery decisions that simultaneously restore feasibility and limit deviation from the original schedule, two objectives inherently in tension under tight time constraints. We formulate recovery as a sequence of neighbourhood-restricted mixed-integer linear programs parameterised by a deviation bound $k$, the $\ell_1$ distance between the recovered and baseline plans. We show that the minimum feasible value $k_{\min}$ can be computed exactly, anchoring the search at its most stable extreme. Rather than targeting a single $k$, we seek a Pareto front of non-dominated recovery plans spanning the stability--cost space, giving dispatchers a structured set of operational options. To navigate this space efficiently, we propose a reinforcement learning policy, trained with the REINFORCE policy-gradient algorithm, that adaptively selects successive values of $k$ from solver feedback, concentrating computational effort in productive regions and terminating exploration when further improvement is unlikely. Evaluated on weekly instances derived from historical data of a Canadian forestry partner, under disruption scenarios covering all event categories considered, the approach recovers richer Pareto fronts with fewer solver calls than fixed-$k$ grid and dichotomic search baselines, and produces feasible recovery plans within operationally acceptable reoptimization times across all configurations and disruption types.

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arXiv2026

Real-World, Large Scale, Multi-Period Log Truck Routing and Scheduling : Application to Canadian Forestry

This paper addresses the multi-period log-truck routing and scheduling problem ($\mathcal{LTRSP}$), a key operational activity in the forestry industry, where transportation accounts for more than one-third of total operational costs. The Canadian forestry sector faces significant logistical difficulties driven by vast geographic distances, seasonal variability, volatile markets, and environmental considerations. Our research tackles a long-standing open question in the forestry operations literature, namely the absence of an exact and scalable formulation of the forestry vehicle routing problem integrating the full range of operational constraints \cite{ronnqvist2015operations}: \textit{How can we model and solve an exact formulation of the forestry VRP problem?} In response, we analyze business rules specific to the forestry sector to construct a routing network reflecting real operational practices, and then propose a comprehensive improved mixed-integer linear programming (MILP) formulation incorporating all known forestry operational constraints, with a detailed justification of key modeling choices such as time discretization, along with a decomposition-based solution methodology tailored for large-scale, multi-period industrial instances. To address the inherent combinatorial complexity, we combine state-of-the-art solvers with a metaheuristic decomposition strategy based on \textit{Relax\&Fix} and \textit{Fix\&Optimize}. Computational experiments on historical data from a Canadian forest company demonstrate near-optimal results within practical computation times, with significant financial gains and reduced greenhouse gas emissions.

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arXiv2026

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

Real-world vehicle routing and scheduling problems involve complex operational rules and feasibility constraints typically formulated as mixed-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 ($\mathcal{LTRSP}$), and embeds each inside a MILP through linearized constraints. We further introduce a stacking mechanism that combines all three within a single augmented optimization problem (AOP), letting the solver endogenously select the most reliable predictor for each decision. On real-world industrial data, each predictor already improves feasibility, but the stacked embedding consistently achieves the lowest objective degradation: it (i)~satisfies the operational rules on unseen instances with smaller degradation than any single-model variant, (ii)~picks the most appropriate predictor per decision without prior knowledge of the rule's nature, and (iii)~reduces daily manual adjustment effort while remaining tractable for daily use. Beyond this application, the framework enables optimization tools that adapt to evolving practice without recurrent manual remodeling.

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arXiv2025

Multi-Representation Attention Framework for Underwater Bioacoustic Denoising and Recognition

Automated monitoring of marine mammals in the St. Lawrence Estuary faces extreme challenges: calls span low-frequency moans to ultrasonic clicks, often overlap, and are embedded in variable anthropogenic and environmental noise. We introduce a multi-step, attention-guided framework that first segments spectrograms to generate soft masks of biologically relevant energy and then fuses these masks with the raw inputs for multi-band, denoised classification. Image and mask embeddings are integrated via mid-level fusion, enabling the model to focus on salient spectrogram regions while preserving global context. Using real-world recordings from the Saguenay St. Lawrence Marine Park Research Station in Canada, we demonstrate that segmentation-driven attention and mid-level fusion improve signal discrimination, reduce false positive detections, and produce reliable representations for operational marine mammal monitoring across diverse environmental conditions and signal-to-noise ratios. Beyond in-distribution evaluation, we further assess the generalization of Mask-Guided Classification (MGC) under distributional shifts by testing on spectrograms generated with alternative acoustic transformations. While high-capacity baseline models lose accuracy in this Out-of-distribution (OOD) setting, MGC maintains stable performance, with even simple fusion mechanisms (gated, concat) achieving comparable results across distributions. This robustness highlights the capacity of MGC to learn transferable representations rather than overfitting to a specific transformation, thereby reinforcing its suitability for large-scale, real-world biodiversity monitoring. We show that in all experimental settings, the MGC framework consistently outperforms baseline architectures, yielding substantial gains in accuracy on both in-distribution and OOD data.

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arXiv2025

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

Accurate modeling of physical systems governed by partial differential equations is a central challenge in scientific computing. In oceanography, 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.

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arXiv2024

Towards a connection between the capacitated vehicle routing problem and the constrained centroid-based clustering

Efficiently solving a vehicle routing problem (VRP) in a practical runtime is a critical challenge for delivery management companies. This paper explores both a theoretical and experimental connection between the Capacitated Vehicle Routing Problem (CVRP) and the Constrained Centroid-Based Clustering (CCBC). Reducing a CVRP to a CCBC is a synonym for a transition from an exponential to a polynomial complexity using commonly known algorithms for clustering, i.e K-means. At the beginning, we conduct an exploratory analysis to highlight the existence of such a relationship between the two problems through illustrative small-size examples and simultaneously deduce some mathematically-related formulations and properties. On a second level, the paper proposes a CCBC based approach endowed with some enhancements. The proposed framework consists of three stages. At the first step, a constrained centroid-based clustering algorithm generates feasible clusters of customers. This methodology incorporates three enhancement tools to achieve near-optimal clusters, namely: a multi-start procedure for initial centroids, a customer assignment metric, and a self-adjustment mechanism for choosing the number of clusters. At the second step, a traveling salesman problem (T SP) solver is used to optimize the order of customers within each cluster. Finally, we introduce a process relying on routes cutting and relinking procedure, which calls upon solving a linear and integer programming model to further improve the obtained routes. This step is inspired by the ruin & recreate algorithm. This approach is an extension of the classical cluster-first, route-second method and provides near-optimal solutions on well-known benchmark instances in terms of solution quality and computational runtime, offering a milestone in solving VRP.

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arXiv2023

Rule-Extraction Methods From Feedforward Neural Networks: A Systematic Literature Review

Motivated by the interpretability question in ML models as a crucial element for the successful deployment of AI systems, this paper focuses on rule extraction as a means for neural networks interpretability. Through a systematic literature review, different approaches for extracting rules from feedforward neural networks, an important block in deep learning models, are identified and explored. The findings reveal a range of methods developed for over two decades, mostly suitable for shallow neural networks, with recent developments to meet deep learning models' challenges. Rules offer a transparent and intuitive means of explaining neural networks, making this study a comprehensive introduction for researchers interested in the field. While the study specifically addresses feedforward networks with supervised learning and crisp rules, future work can extend to other network types, machine learning methods, and fuzzy rule extraction.

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arXiv2023

Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context

Solar power harbors immense potential in mitigating climate change by substantially reducing CO$_{2}$ emissions. Nonetheless, the inherent variability of solar irradiance poses a significant challenge for seamlessly integrating solar power into the electrical grid. While the majority of prior research has centered on employing purely time series-based methodologies for solar forecasting, only a limited number of studies have taken into account factors such as cloud cover or the surrounding physical context. In this paper, we put forth a deep learning architecture designed to harness spatio-temporal context using satellite data, to attain highly accurate \textit{day-ahead} time-series forecasting for any given station, with a particular emphasis on forecasting Global Horizontal Irradiance (GHI). We also suggest a methodology to extract a distribution for each time step prediction, which can serve as a very valuable measure of uncertainty attached to the forecast. When evaluating models, we propose a testing scheme in which we separate particularly difficult examples from easy ones, in order to capture the model performances in crucial situations, which in the case of this study are the days suffering from varying cloudy conditions. Furthermore, we present a new multi-modal dataset gathering satellite imagery over a large zone and time series for solar irradiance and other related physical variables from multiple geographically diverse solar stations. Our approach exhibits robust performance in solar irradiance forecasting, including zero-shot generalization tests at unobserved solar stations, and holds great promise in promoting the effective integration of solar power into the grid.

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arXiv2022

MAgNet: Mesh Agnostic Neural PDE Solver

The computational complexity of classical numerical methods for solving Partial Differential Equations (PDE) scales significantly as the resolution increases. As an important example, climate predictions require fine spatio-temporal resolutions to resolve all turbulent scales in the fluid simulations. This makes the task of accurately resolving these scales computationally out of reach even with modern supercomputers. As a result, current numerical modelers solve PDEs on grids that are too coarse (3km to 200km on each side), which hinders the accuracy and usefulness of the predictions. In this paper, we leverage the recent advances in Implicit Neural Representations (INR) to design a novel architecture that predicts the spatially continuous solution of a PDE given a spatial position query. By augmenting coordinate-based architectures with Graph Neural Networks (GNN), we enable zero-shot generalization to new non-uniform meshes and long-term predictions up to 250 frames ahead that are physically consistent. Our Mesh Agnostic Neural PDE Solver (MAgNet) is able to make accurate predictions across a variety of PDE simulation datasets and compares favorably with existing baselines. Moreover, MAgNet generalizes well to different meshes and resolutions up to four times those trained on.

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arXiv2017

Machine Learning Approaches for Traffic Volume Forecasting: A Case Study of the Moroccan Highway Network

In this paper, we aim to illustrate different approaches we followed while developing a forecasting tool for highway traffic in Morocco. Two main approaches were adopted: Statistical Analysis as a step of data exploration and data wrangling. Therefore, a beta model is carried out for a better understanding of traffic behavior. Next, we moved to Machine Learning where we worked with a bunch of algorithms such as Random Forest, Artificial Neural Networks, Extra Trees, etc. yet, we were convinced that this field of study is still considered under state of the art models, so, we were also covering an application of Long Short-Term Memory Neural Networks.

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