1. explain the basic underlying principles of heuristic search as an optimization method to solve complex problems
2. calculate problem complexity and algorithm complexity
3. distinguish between exact methods and heuristic methods, and when to apply which methods
4. identify the need for heuristics
5. explain the methodology of the most commonly used heuristics (Greedy, Simmulated Annealing, Tabu Search, Evolutionary algorithms, Ant Colony optimization, PSO)
6. explain the advantages and disadvantages of various heuristic methods
7. develop new (hybrid) heuristic methods by applying existing heuristic methods
8. evaluate the quality of the solutions obtained with heuristic methods

Lectures are held in two cycles. The first cycle contains 7 weeks of lectures followed by the second cycle lasting 6 weeks, with a teaching load of 2 hours per week.

Students are assigned 2 laboratory assignments in which they will independently design, implement and test heuristic algorithms for solving given combinatorial optimization problems.

Project: design, implementation and testing of heuristic algorithms for solving a given combinatorial optimization problem.

1. Introduction to optimization; optimization models, combinatorial optimization.
2. Complexity theory; categorization of optimization methods; why and when to use heuristics.
3. Exact methods: integer programming, branch and bound, dynamic programming.
4. Greedy algorithms. Basic concepts of metaheuristics.
5. Local search. Greedy randomized adaptive search procedure (GRASP).
6. Variable neighborhood search. Large neighborhood search.
7. Tabu search.
8. Midterm exam
9. Simulated annealing.
10. Ant colony optimization.
11. Particle swarm optimization. Project assignment.
12. Evolutionary algorithms.
13. Case studies involving hybrid and meta heuristics.
14. Project presentations.
15. Final exam