arXiv subjects

Issmail El Hallaoui

Publications and source records attributed to Issmail El Hallaoui.

4 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.

math.OC↗

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.

math.OC↗

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.

math.OC↗

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.

math.OC↗