654-0908/02 – Optimization (OPT)
| Gurantor department | Department of Industrial Systems Management | Credits | 10 |
| Subject guarantor | doc. Ing. Milan Heger, CSc. | Subject version guarantor | doc. Ing. Milan Heger, CSc. |
| Study level | postgraduate | Requirement | Choice-compulsory type B |
| Year | | Semester | winter + summer |
| | Study language | Czech |
| Year of introduction | 2022/2023 | Year of cancellation | |
| Intended for the faculties | HGF, FMT | Intended for study types | Doctoral |
Subject aims expressed by acquired skills and competences
Student will be able to formulate basic axioms of optimization, will be able to choose among individual accesses and optimization methods in the process of control of technological processes, will be able to design and construct optimal control systems, will be able to apply principles of artificial intelligence in praxis.
Teaching methods
Individual consultations
Project work
Summary
Basic principles of optimization, choices between individual approaches and optimization methods in the management of technological processes, suggestions for optimal control systems, application of principles of artificial intelligence in practice are discussed.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
E-learning
Other requirements
Development of the project on a topic related to the dissertation.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Static optimization - analytical and numerical methods for univariate and multivariate optimization.
2. Using linear programming to optimize control technology and manufacturing processes.
3. Dynamic optimization - dynamic programming, principle Pontrjagin minimum and variational calculus in problems of optimal control.
4. Advanced methods for optimal adjustment of controlers.
5. Modeling, simulation and optimization of selected logistics management issues.
6. Optimization using genetic and evolutionary algorithms.
7. Possibilities of using neural networks in the optimization of technological processes.
8. Creation and application of optimization algorithms.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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