3 papers
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…