This course introduces technologies and practices for simulation modeling of complex systems, with emphasis on discrete-event simulation and queueing models. It covers statistical input modeling, output analysis, and verification and validation. Risk analysis is emphasized throughout by linking simulation outputs to managerial decision-making under uncertainty. R and Python will be used as the primary simulation tools. Students will be able to:
- Identify and explain sources of uncertainty in real-world business problems.
- Explain basic concepts of simulation and its utility in solving risk-related problems.
- Apply mathematical, statistical, and simulation techniques to construct and analyze models.
- Use Monte Carlo and discrete-event simulation to study operations and service systems.
- Adapt and extend R/Python simulation code to new decision contexts.
- Interpret and validate simulation outputs and present risk-informed insights.