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Lecturer(s)
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Course content
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Course contents and syllabus: 1. Systems and their definitions - basic principles of systems thinking and modeling, ways of describing the behavior of models, continuous and discrete systems, experiments. 2. Introduction to quantitative modeling - principles, and possibilities of application. 3. Basic methods of creating models - mathematical methods and simulations. 4. Linear programming, formulation of LP problems - principle, basic concepts, LP applications, graphical solution of LP problems, simplex method, SW tools, interpretation of results. 5. Introduction to nonlinear programming, gradient methods. 6. Application of graph theory - basic concepts, optimization problems on graphs. 7. Game theory - theoretical foundations, classification, games with zero and non-zero sum. 8. Basic concepts of simulation, simulation methods. 9. Event-driven models - state models, collective service systems, optimization tasks. ¨ 10. Models of continuous systems and their applications. 11. Multiagent models - agent approach and possibilities of its application, agent modeling. 12. Application of modeling techniques to selected problems from science and research or company practice. The practical part of the course (tutorials) copies the content of lectures. During the tutorials, students will apply and practice theoretical knowledge from lectures. The use of teamwork and project teaching is also expected.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Demonstration, Laboratory, Practical training, Case studies
- Preparation for exam
- 20 hours per semester
- Semestral paper
- 20 hours per semester
- Preparation for classes
- 34 hours per semester
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Learning outcomes
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The course aims to teach students the selected methods of computer modeling to support decision-making processes to use them in their practice. Students will learn the systems thinking necessary for modeling complex systems and creating and solving mathematical and simulation models of real systems, emphasizing model fidelity and its contribution to understanding the original system and decision support. The mentioned topics will be tested and verified using a team approach and current software products for modeling, optimization, simulation, and decision support at the tutorials.
Upon successful completion students will be able to: - orientate in the area of modeling and simulation systems, and know the techniques and methods of models design - use systems thinking for systems analysis - create models of simple systems - use software tools for simulation support - carry out simulation experiments - cooperate in teams on various positions
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Prerequisites
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A prerequisite for enrollment in this course is knowledge of programming, at least at the level of completing the course OOP I and II.
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Assessment methods and criteria
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Written examination, Analysis of student's work activities (technical works), Analysis of creative work (musical, visual, literary), Test, Seminar work
To complete the course, it is necessary to solve continuous tasks in tutorials, pass a theoretical test, develop and implement a semester project.
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Recommended literature
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BORSHCHEV, Andrei, GRIGORYEV, Ilya. The Big Book of Simulation Modelling, Multimethod Modeling with AnyLogic 8. Anylogic 2020. Dostupné z: https://www.anylogic.com/resources/books/big-book-of-simulation-modeling/.
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GREASLEY, Andrew. Simulation modeling for business. Routledge, 2017. ISBN 9781351899987.
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Jablonský, Josef. Operační výzkum. 3. vyd. Praha : VŠE, 2001. ISBN 80-245-0162-7.
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Law, Averill M. Simulation modeling and analysis. 4th ed. Boston, MA : McGraw-Hill, 2007. ISBN 978-007-125519-6.
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