Logistics
IOLab studies complex routing, transportation, and packing problems arising in operational settings. Our research extends classical models such as the Vehicle Routing Problem to account for heterogeneous fleets, periodic services, intermediate facilities, nonlinear charging, uncertain demand, and other constraints found in practice.
Current work focuses on sustainable waste collection, including the planning of electric-vehicle fleets in collaboration with AcegasApsAmga. The activity combines mathematical models, metaheuristics, and data-driven analysis to design efficient collection plans while reducing environmental impact.
The group also works on transportation and service logistics, including railway capacity estimation for industrial freight junctions and location-routing models for emergency medical services.
Open resources
We publish datasets and validation tools for reproducible research, including instances for periodic waste-collection routing and electric-vehicle routing with nonlinear charging. See the IOLab organization on GitHub and the Smart Waste Collection project.
Luca Di Gaspero
Associate Professor of Information Technology · Director of IOLab
Director of IOLab. He develops intelligent optimization methods and decision-support systems, from metaheuristics and hybrid algorithms to large language models.