Programma

The course will provide the basic knowledge about Constrained Programming and related methodologies for solving real world discrete optimization problems. In particular the topics will be the following ones:

• Constraint Programming basics: fundamental concepts, types of domains (finite domains, intervals, sets), constraints, search, branch and bound.
• CP modeling techniques: global constraints, redundant constraints, symmetry elimination, special-purpose constraints (e.g., scheduling), modeling of optimization problems, problem reduction.
• CP languages/libraries: MiniZinc, ILOG CP Optimizer. Modeling examples: n-Queens, Cryptoarithmetic, Sudoku, Scheduling, Timetabling, ...
• Basic solution methods: propagation, consistency, search. Advanced solution methods: heuristic methods, hybrid approaches, integration with heuristic/metaheuristic techniques. Statistical analysis of optimization algorithms.
• Lab practice.

After successful completion of the course, students are able to:

• model and solve combinatorial optimization problems using Constraint Programming (CP);
• implement and compare different methods for solving CP problems.
• select the best heuristic to improve search performance.