638-0908/05 – Optimization (OPT)

Gurantor departmentDepartment of Automation and Computing in IndustryCredits10
Subject guarantordoc. Ing. Milan Heger, CSc.Subject version guarantordoc. Ing. Milan Heger, CSc.
Study levelpostgraduateRequirementChoice-compulsory type B
YearSemesterwinter + summer
Study languageEnglish
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFMT, HGFIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
HEG30 doc. Ing. Milan Heger, CSc.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 20+0
Part-time Examination 20+0

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


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:

KIRK, D. E. Optimal control theory: an introduction. Dover ed. Mineola: Dover Publications, 2004. ISBN 0-486-43484-2. RUSSELL, S. J. a P. NORVIG. Artificial intelligence: a modern approach. 3rd ed., Pearson new international ed. Harlow: Pearson, 2014. ISBN 978-1-292-02420-2. NORGAARD, M. Neural networks for modelling and control of dynamic systems: a practitioner's handbook. London: Springer, 2000. ISBN 1-85233-227-1.

Recommended literature:

WINTER, G. and ed. Genetic algorithms in engineering and computer science. Chichester: Wiley, 1995. ISBN 0-471-95859-X. BARNES B. a G. R. FULFORD. Mathematical Modelling with Case Studies: Using Maple and Matlab. Chapman and Hall/CRC, 2015. BERTSEKAS, D. P. Nonlinear programming. 2nd ed. Belmont: Athena Scientific, 1999. ISBN 1-886529-00-0.

Way of continuous check of knowledge in the course of semester


Other requirements

Development of the project on a topic related to the dissertation.


Subject has no prerequisities.


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

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

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Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2020/2021 (P0413D270001) Management of Industrial Systems P English Ostrava Choice-compulsory type B study plan
2020/2021 (P0413D270001) Management of Industrial Systems K English Ostrava Choice-compulsory type B study plan
2019/2020 (P0413D270001) Management of Industrial Systems P English Ostrava Choice-compulsory type B study plan
2019/2020 (P0413D270001) Management of Industrial Systems K English Ostrava Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner