The Constrained Optimization Group conducts research on constraint satisfaction and optimization problems with a particular focus on declarative and logic-based methodologies.
Our group develops and applies logic programming and constraint-solving techniques to address complex combinatorial optimization problems including scheduling, timetabling and resource allocation. We develop models capable of handling complex constraints, such as fairness, precedence relations, energy consumption and other real-world limitations using declarative frameworks like Constraint Logic Programming (CLP) and Answer Set Programming (ASP).
More recently, the group has also been exploring the integration of machine learning and deep learning techniques to enhance the effectiveness and scalability of constraint-based problem solving.
The CO group is coordinated by Prof. Marco Gavanelli alongside the research team:
Alessandro Bertagnon, PhD,
Dominik Miotla, PhD. student.
Previous Members:
Giuseppe Cota
Andrea Peano
Rosa Herrero y Anton
Members of the group won the Logic Programming Competition (at the International Conference on Logic Programming, ICLP) in 2015 and 2025.
Best PhD Thesis Award (2019 – 2022) by the Italian Association of Logic Programming (Gruppo Ricercatori e Utenti Logic Programming, GULP), awarded to Alessandro Bertagnon for his thesis titled “Improving Reasoning in Constraint Logic Programming: an Application to Route Planning and Qualitative Temporal Reasoning Problems.”
Best Paper Award at the 27th International Conference on Logic Programming (ICLP 2011) for the article “Optimal placement of valves in a water distribution network with CLP(FD)" by Massimiliano Cattafi, Marco Gavanelli, Maddalena Nonato, Stefano Alvisi, Marco Franchini (Lexington, Kentucky, July 2011).
Marco Gavanelli: Constraint Logic Programming. Tutorial. GULP School on Logic Programming, Genoa (Italy), the 30th of June 2015.