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Lecturer(s)
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Course content
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Course content and syllabus: 1. Systems and their definitions - basic principles of systems thinking and modeling 2. Methods of describing model behavior, continuous and discrete systems, experiments 3. Introduction to quantitative modeling - principles and areas of application 4. Basic methods of model creation - mathematical approaches and simulations 5. Models of continuous systems and their applications 6. Linear programming, formulation of LP problems - principles, basic concepts, LP applications, graphical solution of LP problems, simplex method, software tools, interpretation of results 7. Introduction to nonlinear programming, gradient methods 8. Applications of graph theory - basic concepts, optimization problems on graphs 9. Basic concepts of simulation, simulation methods 10. Event-driven models - state models 11. Queueing systems, optimization problems 12. Multi-agent models - agent modeling, possible applications 13. Application of modeling techniques to selected problems from science, research, or business practice Course content: 1. - 2. Systems - examples of various forms of description of basic types of systems, development environments, and software for models. 3. - 4. Mathematical models and optimization - examples of models and their modifications. 5. - 6. Simulation techniques and models - examples of development environments, data collection and preparation. 7. - 8. Design of an event-driven model - state diagrams, rule-based systems. 9. Design of a simulation model of a real mass service system with spatial arrangement. 10. Continuous system models - system dynamics methodology. 11. - 12. Cellular automata and multi-agent systems - model design for specific systems. 13. Creation of a machine learning-based model - simple deep learning model.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Demonstration, Laboratory, Practical training
- 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), Test, Interim evaluation
To complete the course, it is necessary to solve continuous tasks in tutorials, pass a theoretical test, develop and implement a semester project. During both regular and make-up credit terms, as well as at every exam session, all aids are prohibited except those permitted by the instructor.
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Recommended literature
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Anylogic. AnyLogic 8. 2020.
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BANERJEE, Susmita; BHATTACHARYA, Avik. Discrete and Continuous Simulation: Theory and Practice. Boca Raton. 2018.
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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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