Students learn from the recommended and obligatory literature sources and from the performed state-of-the-art analysis.
Regular consultations with the lecturer are strongly recommended.
Terminated in academic year 2024/2025
Stochastic and fuzzy modelling of decision processes
| Type of study | Doctoral |
|---|---|
| Language of instruction | English |
| Code | 157-9583/01 |
| Abbreviation | SMEPe |
| Course title | Stochastic and fuzzy modelling of decision processes |
| Credits | 10 |
| Coordinating department | Department of Systems Engineering and Informatics |
| Course coordinator | prof. Mgr. Ing. František Zapletal, Ph.D. |
Subject syllabus
1. Different types of uncertainty and their description.
2. Risk and uncertainty in different parts of the decision process.
3. Use of quantitative input data - how to involve random variables into the decision-making process.
4. Use of qualitative uncertain data - qualitative linguistic scales.
5. Pair-wise comparison with uncertain data and its consistency check.
6. Advantages and disadvantages of deterministic/random/fuzzy evaluations and their interpretation (defuzzification, possibility and necessity measures, stochastic dominance, etc.).
7. Missing data in a decision matrix.
8. Selected algorithms for weights evaluation based on uncertain input data.
9. Selected algorithms for alternatives evaluation under uncertain performances (fuzzy-PROMETHEE, fuzzy-AHP, fuzzy-TOPSIS. etc.).
10. Selected algorithms for efficiency evaluation under risk and uncertainty (fuzzy-DEA, stochastic DEA, Fuzzy-PROMETHEE V, etc.).
2. Risk and uncertainty in different parts of the decision process.
3. Use of quantitative input data - how to involve random variables into the decision-making process.
4. Use of qualitative uncertain data - qualitative linguistic scales.
5. Pair-wise comparison with uncertain data and its consistency check.
6. Advantages and disadvantages of deterministic/random/fuzzy evaluations and their interpretation (defuzzification, possibility and necessity measures, stochastic dominance, etc.).
7. Missing data in a decision matrix.
8. Selected algorithms for weights evaluation based on uncertain input data.
9. Selected algorithms for alternatives evaluation under uncertain performances (fuzzy-PROMETHEE, fuzzy-AHP, fuzzy-TOPSIS. etc.).
10. Selected algorithms for efficiency evaluation under risk and uncertainty (fuzzy-DEA, stochastic DEA, Fuzzy-PROMETHEE V, etc.).
E-learning
Literature
CHAVAS, Jean P. Risk Analysis in Theory and Practice, Academic Press Advanced Finance, 2004. ISBN 9780121706210 .
SKALNA, Iwona a kolektiv. Advances in Fuzzy Decision Making: Theory and Practice. Springer, 2015. ISBN 978-3-319-26494-3 .
KAHRAMAN, Cengiz a kolektiv. Fuzzy Multi-Criteria Decision Making, Springer, 2008. ISBN 978-0-387-76813-7 .
SKALNA, Iwona a kolektiv. Advances in Fuzzy Decision Making: Theory and Practice. Springer, 2015. ISBN 978-3-319-26494-3 .
KAHRAMAN, Cengiz a kolektiv. Fuzzy Multi-Criteria Decision Making, Springer, 2008. ISBN 978-0-387-76813-7 .
Advised literature
CARLSSON, Christer a Robert FULLER. Fuzzy Reasoning in Decision Making and Optimization, Springer, 2002. ISBN 3790814288 .
KULKARNI, V. G. Introduction to modeling and analysis of stochastic systems, Springer, 2010. ISBN 1461427355 .
LEVY, Haim. Stochastic Dominance. Springer, 2015. ISBN 3319217070 .
KULKARNI, V. G. Introduction to modeling and analysis of stochastic systems, Springer, 2010. ISBN 1461427355 .
LEVY, Haim. Stochastic Dominance. Springer, 2015. ISBN 3319217070 .