Course detail
Modelling and Simulation of Production Systems
FSI-GMV-KAcad. year: 2023/2024
The aim of the course is to provide students with the practical knowledge in the field of modern methods for the simulation and modelling of production systems in kontext of Industry 4.0. It focuses primarily on technological processes and manufacturing systems and applies the principles of continuous and discrete simulation for their modelling. The application of simulation methods to designing and controlling discrete systems and processes is analysed, in the field of a strategic planning, operational control and ergonomics.
Language of instruction
Czech
Number of ECTS credits
4
Mode of study
Not applicable.
Guarantor
Entry knowledge
The student should be able to:
- knowledge of basic principles of production systems and machines and their functionality
- basic knowledge of mathematics in the field of differential equations, matrix operations and statistics
- Basic PC skills including various types of data processing software (Excel, Matlab, etc.).
- knowledge of basic principles of production systems and machines and their functionality
- basic knowledge of mathematics in the field of differential equations, matrix operations and statistics
- Basic PC skills including various types of data processing software (Excel, Matlab, etc.).
Rules for evaluation and completion of the course
Course-unit credit is conditional on the following:
1. Attendance at exercises (except documented excusable absence)
2. Fulfillment of the conditions of continuous control (preparation for exercise, activity during exercise); these requirements will be specified at the beginning of the semester in practice.
3. Elaboration and demonstration of assigned tasks
Examination:
The exam verifies the acquired knowledge. The exam is combined. In the written part verifies the ability of the student to apply the acquired knowledge and methods in the test and in the oral part if necessary verifies the knowledge of theoretical foundations.
Attendance at obligatory lessons is checked and only substantial reasons of absence are accepted. Missed lessons can be substituted for via solution of extra exercises.
1. Attendance at exercises (except documented excusable absence)
2. Fulfillment of the conditions of continuous control (preparation for exercise, activity during exercise); these requirements will be specified at the beginning of the semester in practice.
3. Elaboration and demonstration of assigned tasks
Examination:
The exam verifies the acquired knowledge. The exam is combined. In the written part verifies the ability of the student to apply the acquired knowledge and methods in the test and in the oral part if necessary verifies the knowledge of theoretical foundations.
Attendance at obligatory lessons is checked and only substantial reasons of absence are accepted. Missed lessons can be substituted for via solution of extra exercises.
Aims
The course aims to provide students with skills in computer simulation systems, especially manufacturing systems. Students will acquire skills in the preparation of simulation models, both continuous and discrete. Furthermore, they learn about methods of linking continuous and discrete modeling.
Students will acquire the knowledge necessary for a deeper understanding of the principles and methods used in simulation. They will learn to apply this knowledge in the creation of simulation models in the field of technical, especially production systems.
Students should be able to prepare a simulation model of production, to process and evaluate various alternatives.
Students will acquire the knowledge necessary for a deeper understanding of the principles and methods used in simulation. They will learn to apply this knowledge in the creation of simulation models in the field of technical, especially production systems.
Students should be able to prepare a simulation model of production, to process and evaluate various alternatives.
Study aids
Not applicable.
Prerequisites and corequisites
Not applicable.
Basic literature
A. Alan B. Pritsker, Claude D. Pegden: Introduction to Simulation and SLAM II
AIM Manuals
Averill M. Law, W. David Kelton: Simulation Modeling and Analysis
Jerry Banks, Discrete-Event System Simulation
AIM Manuals
Averill M. Law, W. David Kelton: Simulation Modeling and Analysis
Jerry Banks, Discrete-Event System Simulation
Recommended reading
Kuneš Jozef a kol.: Základy modelování ,SNTL , 1989
Neuschl Štefan a kol.: Modelovanie a simulácia, ALFA, 1989
Zítek Pavel: : Simulace dynamických systémů , SNTL, 1989
Neuschl Štefan a kol.: Modelovanie a simulácia, ALFA, 1989
Zítek Pavel: : Simulace dynamických systémů , SNTL, 1989
Classification of course in study plans
- Programme N-VSR-K Master's 1 year of study, summer semester, compulsory-optional
Type of course unit
Guided consultation in combined form of studies
17 hod., compulsory
Teacher / Lecturer
Syllabus
1. Introduction to modeling and simulation of systems. Analysis and classification of systems.
2. Basic concepts of systems theory. Classification of models. Basic methods of modeling continuous and discrete systems. Software possibilities of system modeling.
3. Mathematical foundations of modeling and simulation.
4. Methods of modeling discrete systems.
5. Methods of modeling discrete systems.
6. Methods of modeling discrete systems.
7. Methods of event controlled simulation, creating and using of calendar events
8. Modeling of stochastic systems, using of statistical methods, generation of random variables.
9. Connections of real technical systems of production systems in discrete simulation.
10. Connections of real technical systems of production systems in discrete simulation.
11. Evaluation and visualization of simulation results.
12. Interconnection of real and simulated systems, data exchange, model verification.
13. Current trends in modeling and simulation of systems
2. Basic concepts of systems theory. Classification of models. Basic methods of modeling continuous and discrete systems. Software possibilities of system modeling.
3. Mathematical foundations of modeling and simulation.
4. Methods of modeling discrete systems.
5. Methods of modeling discrete systems.
6. Methods of modeling discrete systems.
7. Methods of event controlled simulation, creating and using of calendar events
8. Modeling of stochastic systems, using of statistical methods, generation of random variables.
9. Connections of real technical systems of production systems in discrete simulation.
10. Connections of real technical systems of production systems in discrete simulation.
11. Evaluation and visualization of simulation results.
12. Interconnection of real and simulated systems, data exchange, model verification.
13. Current trends in modeling and simulation of systems
Guided consultation
35 hod., optionally
Teacher / Lecturer
Syllabus
1. Simulation languages, overview of basic tools for description of models and experiments. Basic principles of implementation of simulation systems.
2. Continuous modeling - creating a model and working with it.
3. Continuous modeling - creating a model and working with it.
4. Discrete modeling, event-driven modeling, preparation and creation of models.
5. Discrete modeling, event-driven modeling, preparation and creation of models.
6. Discrete modeling, event-driven modeling, preparation and creation of models.
7. Methods of connecting continuous and discrete modeling.
8. Methods of connecting continuous and discrete modeling.
9. Formal requirements for the model as part of the supply chain
10. Formal requirements for the model as support for the management of production systems
11. Student project - assignment.
12. Methods of modeling production systems under the condition of using typical sensory data; ways of connecting the model with data in off-line and on-line mode in the context of Industry 4.0; Using the model for design and planning.
13. Student project review
2. Continuous modeling - creating a model and working with it.
3. Continuous modeling - creating a model and working with it.
4. Discrete modeling, event-driven modeling, preparation and creation of models.
5. Discrete modeling, event-driven modeling, preparation and creation of models.
6. Discrete modeling, event-driven modeling, preparation and creation of models.
7. Methods of connecting continuous and discrete modeling.
8. Methods of connecting continuous and discrete modeling.
9. Formal requirements for the model as part of the supply chain
10. Formal requirements for the model as support for the management of production systems
11. Student project - assignment.
12. Methods of modeling production systems under the condition of using typical sensory data; ways of connecting the model with data in off-line and on-line mode in the context of Industry 4.0; Using the model for design and planning.
13. Student project review