Matheuristics for Combinatorial Optimization Problems (Module 1)
A.Y. 2025/2026
Course offered to students on the PhD programme in
Visit the PhD website for the course schedule and other information
Lead instructor: Roberto Cordone
"Combinatorial Optimization is a huge domain of study, focused on optimization problems with a finite set of solutions.
It has important practical applications to manifold fields, including artificial intelligence, machine learning,
routing, scheduling, location, network analysis and design.
As many Combinatorial Optimization problems are NP-hard, heuristics are a natural solution approach.
Matheuristics, also known as model-based heuristics,
exploit the information provided by mathematical programming models, that is the representation of the feasible solution space by means of equalities and inequalities on suitable decision variables.
The advantage of these methods with respect to the classical solution-based heuristics and metaheuristics consists in the additional information they give, for example in terms of a priori or a posteriori
guarantees on the quality of the solution returned.
The first module of the course surveys the matheuristics based on relaxation methods and decomposition methods. The second module of the course reviews the matheuristics which exploit the availability
of mathematical programming solvers and those that interact with solution-based metaheuristics."
It has important practical applications to manifold fields, including artificial intelligence, machine learning,
routing, scheduling, location, network analysis and design.
As many Combinatorial Optimization problems are NP-hard, heuristics are a natural solution approach.
Matheuristics, also known as model-based heuristics,
exploit the information provided by mathematical programming models, that is the representation of the feasible solution space by means of equalities and inequalities on suitable decision variables.
The advantage of these methods with respect to the classical solution-based heuristics and metaheuristics consists in the additional information they give, for example in terms of a priori or a posteriori
guarantees on the quality of the solution returned.
The first module of the course surveys the matheuristics based on relaxation methods and decomposition methods. The second module of the course reviews the matheuristics which exploit the availability
of mathematical programming solvers and those that interact with solution-based metaheuristics."
Undefined
Assessment methods
Giudizio di approvazione
Assessment result
superato/non superato
How to enrol
Deadlines
The course enrolment deadline is usually the 27th day of the month prior to the start date.
How to enrol
- Access enrolment on PhD courses online service using your University login details
- Select the desired programme and click on Registration (Iscrizione) and then on Register (Iscriviti)
Ignore the option "Exam session date” that appears during the enrolment procedure.
Contacts
For help please contact [email protected]