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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

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. 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.

Prerequisits

Fundamentals of quantitative methods and statistics.

  • red. prof. dr. ANDREJ ŠKRABA, univ. dipl. org.

  • Written examination: 80
  • Completed homeworks: 20

  • : 39
  • : 24
  • : 117

  • Slovenian
  • Slovenian

  • ORGANIZATION AND MANAGEMENT OF INFORMATION SYSTEMS - 3rd