Czech

Not applicable.

Students will be familiar with fundamental terms, methods and analytical techniques related to decision making models including risk elements. Specific ways of reasoning, 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 of being able to use applied software. The course content is linked to RSMAT course taught in the previous semester.

Not applicable.

Teaching is carried out through lectures and seminars. Lectures consist of interpretations of basic principles, methodology of given discipline, problems and their exemplary solutions. Seminars particularly support practical mastery of subject matter presented in lectures or assigned for individual study with the active participation of students.

Course-unit credit requirements: active participation in seminars, mastering the semestral work, and delivery of semester assignment. Examination (written form): a practical part (4 tasks), a theoretical part (4 tasks); ECTS evaluation 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 solution improving methods.
5. Selected decision making models - software for optimization
6. Decision making models under risk and uncertainty - deterministic reformulations and their properties.
7. Decision making models under risk and uncertainty - engineering applications.
8. Decision making models 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 studied models and methods in the related application areas.

Attendance at seminars is controlled and the teacher chooses the compensation of absences.

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.

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