Industrial Production Scheduling
IOLab develops optimization methods for complex production environments, where scheduling decisions must account for technological constraints, limited resources, energy consumption, and operational objectives.
From 2017 to 2021, the group conducted a research collaboration with Danieli Automation on production scheduling for steel plants. Led by Luca Di Gaspero, the project studied metaheuristic methods for industrial decision-support systems in an Industry 4.0 setting.
More recent work investigates the Oven Scheduling Problem and related parallel-batch scheduling models. The group has developed local-search and large-neighborhood-search methods, as well as instance-space analysis and algorithm-selection techniques for understanding when different algorithms perform best.
Code and experimental data are available through the OSP-LS and OSP-LNS repositories.
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.
Francesca Da Ros
Postdoctoral Researcher at CRO Aviano · Research Collaborator at IOLab
Francesca Da Ros is a postdoctoral researcher at CRO Aviano and a research collaborator at IOLab.