Introduction to Optimization Methods
| Type of study | Follow-up Master |
|---|---|
| Language of instruction | Czech |
| Code | 342-6524/01 |
| Abbreviation | UdOM |
| Course title | Introduction to Optimization Methods |
| Credits | 7 |
| Coordinating department | Institute of Transport |
| Course coordinator | doc. Ing. Dušan Teichmann, Ph.D. |
Subject syllabus
1. Introduction to optimization methods, categorization of optimization methods
2. Graph theory methods – terminology, application of methods for identifying optimal paths.
3. Graph theory methods – application of methods regarding optimal flows in transport networks, graph vertex coloring problems and their use in air transport (optimizing the number of check-in counters, optimizing aircraft fleet utilization, etc.).
4. Linear programming and examples of the most frequently used methods – simplex method.
5. Linear programming and examples of the most frequently used methods – branch and bound method.
6. Nonlinear programming and examples of the most frequently used methods.
7. Dynamic programming and examples of the most frequently used methods – Bellmann's optimality principle.
8. Multicriteria optimization and examples of the most frequently used methods (scalarization method, stepwise optimization method, integration into one criterion method, STEM method)
9. Optimization methods allowing work with uncertainty – stochastic programming.
10. Optimization methods allowing work with uncertainty – fuzzy.
11. Heuristic methods – simple heuristics and their principles.
12. Heuristic methods – metaheuristics and metaheuristic strategies (simulated annealing, tabu search, genetic algorithms).
13. Neural networks and their principles.
2. Graph theory methods – terminology, application of methods for identifying optimal paths.
3. Graph theory methods – application of methods regarding optimal flows in transport networks, graph vertex coloring problems and their use in air transport (optimizing the number of check-in counters, optimizing aircraft fleet utilization, etc.).
4. Linear programming and examples of the most frequently used methods – simplex method.
5. Linear programming and examples of the most frequently used methods – branch and bound method.
6. Nonlinear programming and examples of the most frequently used methods.
7. Dynamic programming and examples of the most frequently used methods – Bellmann's optimality principle.
8. Multicriteria optimization and examples of the most frequently used methods (scalarization method, stepwise optimization method, integration into one criterion method, STEM method)
9. Optimization methods allowing work with uncertainty – stochastic programming.
10. Optimization methods allowing work with uncertainty – fuzzy.
11. Heuristic methods – simple heuristics and their principles.
12. Heuristic methods – metaheuristics and metaheuristic strategies (simulated annealing, tabu search, genetic algorithms).
13. Neural networks and their principles.