157-0372/01 – Optimization Methods (OM)
| Gurantor department | Department of Systems Engineering | Credits | 6 |
| Subject guarantor | doc. Mgr. Ing. František Zapletal, Ph.D. | Subject version guarantor | doc. Mgr. Ing. František Zapletal, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 1 | Semester | summer |
| | Study language | Czech |
| Year of introduction | 2015/2016 | Year of cancellation | |
| Intended for the faculties | EKF | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
The aim of the course is to present advanced optimization methods to students. In particular, an emphasis is put on optimization under risk and uncertainty and efficiency evaluation.
Teaching methods
Lectures
Tutorials
Summary
Students learn both the theoretical background and possibilities of applications in practice. They will get know how to define a mathematical optimization model when risk (stochastic programming) and uncertainty (fuzzy programming) are involved and how to solve these models using software (Solver, GAMS).
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
Zápočet:
- aktivní účast na cvičení
- účast na cvičení alespoň 70 %
- získání minimálně 23 bodů ze 45
Zkouška:
- ústní
E-learning
Studijní opory TpB:
https://lms.vsb.cz/course/view.php?id=72589
Studenti čerpají z povinné a doporučené literatury. Navíc mají k dispozici rozšířené podklady k přednáškám a sbírku řešených příkladů s komentáři.
Other requirements
Active attendance at seminars, study at home, study of mandatory literature, individual consultations.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1) Linear programming problems and their solving
2) Conditions for existence of an optimal solution
3) Stochastic programming - principles, presumptions, applications
4) Stochastic programming models without involving the risk measure
5) Stochastic programming - single stage model with probability constraints
6) Stochastic programming - penalization in the objective function
7) Stochastic programming - models with risk measures
8) Uncertainty and fuzzy sets, basics of fuzzy algebra.
9) Fuzzy optimization - possibilistic mean values, types of uncertainty, alpha-cut approach, possibility, and necessity measures.
10) Flexible programming
11) Introduction to Data Envelopment Analysis (DEA).
12) Basic DEA models and their assumptions.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction