Objectives and competences
- The main objective of the course is to introduce the application of discrete simulation and system dynamics at solving of the organizational problems
- Understand the methods and techniques of modeling by the principles of discrete event simulation and system dynamics
- Learn the quantitative approach to the discrete event models building and system dynamics models
- Learn the basics of simulation languages
- Study the experimental design approaches and interpretation of the results
- Conduct of the complete project in the field of discrete event simulation and system dynamics on the academic case.
Content (Syllabus outline)
- System simulation and solutions of business and organizational problems
- Discrete event simulation
- Stochastic variables and probability function
- Probability distributions and random number generation
- 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
- Continuous simulation and system dynamics
- Differential and difference equations in simulation
- Causal Loop Diagrams and systems’ reference mode
- Dynamical hypothesis
- Development of system dynamics models
- Data collection, statistical calculations and analysis of results
- Testing and validation of models
- Experimental design
- Introduction to agent-based models
- Modeling of micro logistic processes by cyber-physical systems
- Overview of simulation languages: FlexSim, Powersim, AnyLogic, GPSS
- Application of JavaScript programming language for development of simulation models
- Simulation examples
Learning and teaching methods
• lectures
• tutorial
Intended learning outcomes - knowledge and understanding
Knowledge and understanding:
• Quantitative modeling of management problems
• Input data analysis, preparation and statistical processing
• Definition of criterions and dynamical hypothesis testing at the solution selection
• Knowledge and ability to use simulation methods and tools, both discrete and continuous
Intended learning outcomes - transferable/key skills and other attributes
- Complete design and control of discrete and continuous processes
- Building of discrete event simulation models
- Building of system dynamics models
- Connection of the simulation models with databases and production information systems
- Harmonization of production processes
- Elimination of bottle-necks in production processes
- Analysis of structure and response of the system by the aid of system dynamics
Readings
1. Downey, A. (2023). Modeling and simulation in Python: an introduction for scientists and engineers (str. XXVI, 248). No Starch Press.
2. Li, R., & Nakano, A. (2022). Simulation with python: develop simulationand modeling in natural science, engineering, and social sciences (str. XV, 166). Apress.
3. Sterman, J. D. (2000) Business Dynamics: Systems Thinking and Modeling for a Complex World, Irwin/McGraw-Hill.
4. Severance, F. L. (2001) System Modeling and Simulation: An Introduction, John Wiley & Sons, Chichester.
5. 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%)
