157-9583/01 – Stochastic and fuzzy modelling of decision processes (SMEPe)
| Gurantor department | Department of Systems Engineering and Informatics | Credits | 10 |
| Subject guarantor | doc. Mgr. Ing. František Zapletal, Ph.D. | Subject version guarantor | doc. Mgr. Ing. František Zapletal, Ph.D. |
| Study level | postgraduate | Requirement | Choice-compulsory type B |
| Year | | Semester | winter + summer |
| | Study language | English |
| Year of introduction | 2020/2021 | Year of cancellation | |
| Intended for the faculties | EKF | Intended for study types | Doctoral |
Subject aims expressed by acquired skills and competences
The aim of this subject is to provide methodological basis for decision-making in systems, which are uncertain.
Teaching methods
Lectures
Individual consultations
Summary
Students will be able to choose a suitable approach how to model risk or uncertainty and how to build the corresponding mathematical model. Students will learn especially algorithms of multi-criteria decision making with uncertain or random input data, or with missing data.
Compulsory literature:
Recommended literature:
Additional study materials
Way of continuous check of knowledge in the course of semester
Current-state-of-the-art analysis connecting the subject together with the future dissertation thesis.
Written test in the form of a case study.
Oral exam.
E-learning
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.
Other requirements
Proactive approach.
Current-state-of-the-art analysis connecting the subject together with the future dissertation thesis.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
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.).
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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