Czech

5

Not applicable.

Students will become familiar with fundamental terms, methods and analytical techniques related to decision-making models, including risk elements. Specific ways of reasoning that are typical for decision-making under uncertainty and risk will be developed and enhanced.

Basic knowledge of calculus at the Bachelor’s level; elementary knowledge of computer technology at the level needed to be able to use application software. The course content is linked to the RSMAT course taught in the previous semester.

Not applicable.

Tuition takes place via lectures and seminars. The lectures focus on the explanation of basic principles, the methods of the given discipline, problems and example solutions. The seminars mainly support practical mastery of the subject matter presented in lectures or assigned for individual study with the active participation of students.

Course unit credit requirements: active participation in seminars, mastery of the course content, and the submission of a semestral assignment. Examination (written): practical part (4 tasks), theoretical part (4 tasks); the ECTS evaluation scale will be used.

1. Selected deterministic models: linear and nonlinear optimization.
2. Selected models of multicriteria optimization and decision-making.
3. Selected engineering optimization models: inverse problems.
4. Selected deterministic, stochastic, and heuristic methods of finding and improving solutions.
5. Selected decision-making models - optimization software.
6. Models of decision-making under risk and uncertainty - deterministic reformulations and their properties.
7. Mathematical models of decision-making under risk and uncertainty - engineering applications.
8. Mathematical models of decision-making under risk and uncertainty - discrete mathematics applications.
9. Selected stochastic decision-making models for network flows.
10. Multistage models and dynamic programming.
11. Dynamic models - scenario-based techniques, applications in GAMS.
12. Mathematical modelling of advanced decision-making structures.
13. Decision making models for conflicts; model transformations and approximations.

Not applicable.

Students will obtain useful knowledge of decision-making models involving uncertainty and risk. They will also learn how to apply the studied models and methods in the related areas of application.

Attendance at seminars is monitored, and the teacher chooses the manner in which absences are compensated for.

Not applicable.

Not applicable.

KLAPKA A KOL.: Metody operačního výzkumu, VUTIUM 2001, ISBN 80-214-1839-7
POPELA, P.: Nonlinear Programming, University of Malta, učební texty ÚM VUT v Brně, 2001.
POPELA, P.: Stochastic Programming, University of Malta, učební texty ÚM VUT v Brně, 2003.
POPELA, P.: Stochastic Programming, University of Malta, učební texty ÚM VUT v Brně, 2003.

MINOUX, M.: Mathematical Programming, Wiley, 1988, ISBN 0471901709
KALL, P., WALLACE, S.W.: Stochastic Programming, Wiley 1993, ISBN 0471951080
WILLIAMS, H. P.: Model Building in Mathematical Programming, Wiley 1993, ISBN 0471941115.

26 hours, compulsory