Course detail
Optimization Methods I
FSI-FOA Acad. year: 2021/2022 Summer semester
The introductory part of this course deals with systems theory and systems analysis. It explains the essence of a system and relationships between the system and its environment. The next part of this course, operations research, presents tools for solving various types of decision problems. This part shows possibilities of optimizing structure and behaviour of systems, and gives foundations for applying the system approach to solving decision problems. On one hand, the course is focused on typical problems of socio-technical systems, and on the other hand on theoretical and application aspects of solution methods. The course gives foundations for applying the system approach to solving decision problems.
Language of instruction
Czech
Number of ECTS credits
5
Supervisor
Department
Learning outcomes of the course unit
Students will be able to distinguish different kinds and types of systems, and will acquire knowledge of ways of their modelling. They will be able to use a system approach to solving problems, and will acquire knowledge of basic techniques and tools for analysis, synthesis and optimization of systems. Students will have a clear overview of operations research models and methods. They will be able to choose a proper approach to decision problem solving, and construct mathematical models for solving practical problems. They will acquire knowledge of fundamental principles of operations research methods, and will be able to solve operations research problems by means of computer.
Prerequisites
Linear algebra, differential calculus, probability theory, mathematical statistics.
Planned learning activities and teaching methods
The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused on practical topics presented in lectures.
Assesment methods and criteria linked to learning outcomes
Course-unit credit: Active participation in the seminars, elaboration of a given project. Examination: Written.
Aims
To explain basic approaches to modelling socio-technical systems and their effective management. To provide students with an overview of models, methods and applications of operations and systems analysis. To teach constructing mathematical models for solving practical problems. To explain theoretical foundations of operations and systems analysis and principles of working basic methods. To teach using acquired knowledge to design, implementation and management of systems.
Specification of controlled education, way of implementation and compensation for absences
Attendance at seminars is controlled. An absence can be compensated for via solving additional problems.
The study programmes with the given course
Programme N-AIŘ-P: Applied Computer Science and Control, Master's, compulsory
Type of course unit
Lecture
39 hours, optionally
Teacher / Lecturer
Syllabus
1. Basic notions of systems theory, classification of systems.
2. Modelling systems. Systems analysis and operations research.
3. Linear programming problems and their properties.
4. Methods of solving linear programming problems.
5. Sensitivity analysis and duality.
6. Transportation and distribution problems.
7. Formulation and properties of nonlinear programming problems. Optimality conditions.
8. Methods of solving nonlinear programming problems.
9. Integer programming problems, branch-and-bound method.
10. Stochastic optimization problems.
11. Multicriteria decision problems.
12. Problems and methods of game theory.
13. Models of queueing systems.
Exercise
12 hours, compulsory
Teacher / Lecturer
Syllabus
1. Models of systems, systems analysis.
2. Formulating optimization models.
3. Linear problems, graphical solution.
4. Solving linear problems by means of simplex method.
5. Solving transportation problems.
6. Solving nonlinear problems by means of Kuhn-Tucker conditions.
Computer-assisted exercise
14 hours, compulsory
Teacher / Lecturer
Syllabus
1. Formulating optimization models and their solving in MS Excel.
2. Formulating optimization models and their solving in GAMS.
3. Formulating and solving linear programming models.
4. Solving nonlinear and integer programming problems.
5. Solving stochastic optimization problems.
6. Solving multicriteria problems and problems of game theory.
7. Solving queueing problems.