Course detail
Optimization I
FSI-VO1-KAcad. year: 2010/2011
The course deals with the following topics: Operations research, its methodology and relations to system theory and cybernetics. Systems modelling. Systems analysis tasks. Optimization problems. Formulation and properties of optimization problems. Simplex method. Artificial basis applications. Non-linear and convex problems. Quasi-convex programming. Dynamic programming of discrete deterministic processes. Critical Path Method. Examples of applications of operations research methods in technical and economic practice.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Learning outcomes of the course unit
<b>Skills: </b>Students will be able to formulate simple problems of operational research from the practice of mechanical engineering and economics. They will be able to create mathematical models for the above problems, to apply basic methods for their solution and to realise the methods by aids of contemporary tools of computer science.
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
KLAPKA, J., PIŇOS, P.: Decision support system for multicriterial R&D and information systems projects selection. European Journal of Operational Research. 2002, vol. 140, is. 2, s. 434-446. Dostupný z WWW: .
LITTLECHILD, S.; SHUTLER, M. (eds.): Operations Research in Management. Prentice Hall, New York, pp. 298, 1991. ISBN 0-13638-8183
SKYTTNER, L.: General Systems Theory. An Introduction. Macmillan Press, London, pp. 290, 1996. ISBN 0-333-61833-5.
Recommended reading
Classification of course in study plans
Type of course unit
Guided consultation
Teacher / Lecturer
Syllabus
2. Problems of the systems analysis. Optimization problems.
3. Formulations and properties of the linear programming problems.
4. Basic theorem of linear programming.
5. Simplex method and its deduction and derivation.
6. Artificial basis method (two-phase simplex method).
7. Dual problem and sensitivity analysis.
8. Convex non-linear problems.
9. Quasiconvex programming.
10. Bellman Optimality Principle.
11. Dynamic programming of discrete deterministic processes and its applications.
12. Basics of network analysis. Critical Path Method.
13. Multicriterial Optimization and Multicriterial Selection.