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.
- 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
