Objectives and competences
1. Introduce the students to the principles of computer simulation and optimization at holistic resolving of complex problems
2. Present the methods and techniques of modeling and model implementation by simulation languages
3. Learn the basics of simulation languages
4. Present the usefulness of the simulation models at the comprehensive solutions of organizational problems
5. Students acquire competence in the use of IoT data to optimise the sustainability and digital efficiency of systems.
Content (Syllabus outline)
1. Relevance of simulation model application in management science and usage for decision support
2. System simulation and optimization
3. Stochastic variables and probability function
4. Probability distributions and random number generation
5. Probability distribution and generation of random variables
6. Uniform, exponential and empirical distribution
7. Model of server systems
8. Distributions of Inter-arrival times and processing times
9. Queuing disciplines
10. Generation of inter-arrival times and processing times
11. Data collection and analysis of results
12. Overview of simulation languages: AnyLogic, FlexSim, SciLab
13. Application of JavaScript programming language for development of simulation models
14. Simulation examples in the field of organizational sciences and sustainable development
15. Optimising system performance based on real data captured from IoT networks with ESP32 modules
16. Using IoT data to improve the performance and reduce the environmental impact of simulated systems
Learning and teaching methods
• lectures
• tutorial
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
Intended learning outcomes - transferable/key skills and other attributes
- Ability of application of simulation methods and tools for managerial problem solving
- Elimination of bottle-necks
- Holistic design and management of the processes
Readings
1. Ciaburro, G. (2022). Hands-on simulation modeling with Python: develop simulation models for improved efficiency and precision in the decision-making process (2nd ed., str. XIX, 439). Packt.
2. Discrete-event system simulation (5th ed., str. XVIII, 622). (2010). Prentice Hall.
3. Severance, F. L. (2001) System Modeling and Simulation: An Introduction, John Wiley & Sons, Chichester.
4. Law, A. M. (2024). Simulation modeling and analysis (6th ed., str. XXI, 657 , 8 pril.). McGraw-Hill.
Additional information on implementation and assessment written exam (80%)
coursework (20%)
