arXiv subjects

Kaoutar Bouaachra

Publications and source records attributed to Kaoutar Bouaachra.

3 recordsLinked to original sources

arXiv2026

Lexicographic Minimax Load Balancing for T-Adaptive Segment Routing

We introduce the T-Adaptive Segment Routing, a multi-period optimization problem that emerges in the traffic engineering of core IP/MPLS networks when scheduled maintenance operations are involved. The problem seeks a sequence of Segment Routing (SR) configurations that can adapt to a multi-period scheduled maintenance, while allowing limited number of path reconfigurations between successive time steps. Instead of only minimizing the classical Maximum Link Utilization (MLU) criteria, we propose a more refined lexicographic objective that minimizes the sorted vector of link loads in its entirety, thereby enabling a more efficient resource utilization across all links. To tackle this problem, we develop three exact formulations for solving the resulting lexicographic optimization problem. Among these, the Stela and Carla formulations provide the most favorable computational basis for trajectory-based optimization. We thus develop a trajectory column-generation scheme for these formulations, using pricing oracles that vary from heuristic to exact. Exact pricing gives an exact solution of the LP relaxation of the trajectory master, but it does not itself give a certificate of integer optimality: solving the integer master over the columns generated at the root node only certifies optimality within the generated column pool. To overcome this gap, we incorporate trajectory column generation into a Branch-and-Price framework. The branching rule operates on aggregate variables representing segment usage, which correspond to the original compact routing variables. Thus, Branch-and-Price guarantees global integer optimality for every Stela and Carla rank, and when all ranks are solved to exactness, it guarantees lexicographic optimality of the final solution.

math.OC↗

arXiv2026

On the T-Adaptive Segment Routing Problem

In this paper, we present a multi-period optimization problem arising from the traffic engineering of core IP/MPLS networks, called T-ASR. This problem aims at computing a sequence of segment routing paths that adapt to a multi-period scheduled maintenance, while allowing limited number of path reconfigurations between successive time steps. Rather than focusing solely on minimizing the classical Maximum Link Utilization (MLU) criteria, we introduce a more refined lexicographic objective that minimizes the entire sorted vector of link loads providing a more efficient resource utilization for all the links. We propose a generic approach that decomposes this problem into a sequence of subproblems. To model each subproblem, we introduce two Mixed Integer Linear Programming (MILP) formulations, ALEXA and STELA, resulting in two variants of our approach. Finally, we evaluate and compare the efficiency of both variants on small to medium-sized network instances.

math.OC↗

arXiv2026

Spatio-temporal modelling of electric vehicle charging demand

Accurate forecasting of electric vehicle (EV) charging demand is critical for grid management and infrastructure planning. Yet the field continues to rely on legacy benchmarks; such as the Palo Alto (2020) dataset; that fail to reflect the scale and behavioral diversity of modern charging networks. To address this, we introduce a novel large-scale longitudinal dataset collected across Scotland (2022 2025), which release it as an open benchmark for the community. Building on this dataset, we formulate EV charging demand as a spatio-temporal latent Gaussian field and perform approximate Bayesian inference via Integrated Nested Laplace Approximation (INLA). The resulting model jointly captures spatial dependence, temporal dynamics, and covariate effects within a unified proba bilistic framework. On station-level forecasting tasks, our approach achieves competitive predictive accuracy against machine learning baselines, while additionally providing principled uncertainty quan tification and interpretable spatial and temporal decompositions properties that are essential for risk-aware infrastructure planning.

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