Objectives and competences
- Introduce the students to the principles of computer simulation and optimization at holistic resolving of complex problems
- Present the methods and techniques of modeling and model implementation by simulation languages
- Learn the basics of simulation languages
- Present the usefulness of the simulation models at the comprehensive solutions
Content (Syllabus outline)
- Relevance of simulation model application in management science and usage for decision support
- System simulation and optimization
- Stochastic variables and probability function
- Probability distributions and random number generation
- Probability distribution and generation of random variables
- Uniform, exponential and empirical distribution
- Model of server systems
- Distributions of Inter-arrival times and processing times
- Queuing disciplines
- Generation of inter-arrival times and processing times
- Data collection and analysis of results
- Overview of simulation languages: AnyLogic, FlexSim, SciLab
- Application of JavaScript programming language for development of simulation models
- Simulation examples
Intended learning outcomes - knowledge and understanding
After successful completion of the course the student will be able to:
• Develop quantitative models of management processes
• Develop dynamic models of organizational systems by the principles of discrete event simulation
• Define efficiency criteria in simulation models
• Conduct statistical test of hypotheses at the process of best solution selection
• Apply simulation methodology and tools at the optimization of the organizational systems
Readings
1. Borschev A. (2013) The Big Book of Simulation Modeling: Multimethod Modeling with Anylogic 6, AnyLogic North America.
2. Ciaburro G. (2020) Hands-On Simulation Modeling with Python: Develop simulation models to get accurate results and enhance decision-making processes, Packt Publishing.
3. Gordon S. I., Guilfoos B. (2017) Introduction to Modeling and Simulation with MATLAB® and Python, Chapman & Hall/CRC.
4. Banks, J., Carson, J. S., Nelson, B. L., Nicol, D. M. (2009). Discrete-Event System Simulation, Prentice Hall.
5. Severance, F. L. (2001) System Modeling and Simulation: An Introduction, John Wiley & Sons, Chichester.
6. Kljajić, M., Bernik, I., Škraba, A. (1999) Dogodkovna simulacija sistemov, zapiski predavanj.
