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

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

math.OC2026

STREGO: A Scalable Implementation of a Trust-Region Framework for Noisy Bayesian Optimization

Youssef Diouane, Issmail El Hallaoui, Meisseme Kadri +1

TREGO, a trust-region Bayesian optimization (BO) method for expensive blackbox problems, has convergence guarantees, but only for exact function values. In high dimension, its perf…

math.OC2026

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

Abdelhakim Abdellaoui, Issmail El Hallaoui, Loubna Benabbou +2

We consider the real-time reoptimisation of log-truck routing and scheduling in the Canadian forestry industry following unforeseen disruptions. Road closures, vehicle breakdowns,…

math.OC2026

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

Abdelhakim Abdellaoui, Issmail El Hallaoui, Loubna Benabbou +2

This paper addresses the multi-period log-truck routing and scheduling problem (), a key operational activity in the forestry industry, where transportation accoun…

math.OC2026

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

Abdelhakim Abdellaoui, Ayoub Boufous, Issmail El Hallaoui +3

Real-world vehicle routing and scheduling problems involve complex operational rules and feasibility constraints typically formulated as mixed-integer linear programs (MILP). Howev…

math.OC2024★ 1 cited

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

Abdelhakim Abdellaoui, Loubna Benabbou, Issmail El Hallaoui

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 exp…