OA3312 Simulation Modeling and Analysis

This course introduces discrete event simulation, emphasizing event-graph and process-oriented modeling, input modeling, and rigorous verification and validation. Students design and implement simulation models, analyze simulation input uncertainty, evaluate system performance, and support operational decision-making.

Lecture Hours

4

Lab Hours

0

Course Learning Outcomes

• Formulate and implement simulation models of moderately large stochastic systems, choosing appropriately between event-scheduling and process-oriented worldviews.
• Develop stochastic input models from data and apply queueing-theoretic results to inform model construction and validate simulation outputs.
• Verify and validate simulation models, and apply statistically sound methods for simulation output analysis.
• Design simulation experiments using factorial, fractional factorial, central composite, and space-filling designs, and justify design choices based on study goals and constraints.
• Construct and apply meta-models to characterize system behavior, support optimization, and inform robust decisions.
• Evaluate the appropriateness, validity, and limitations of simulation studies, including modeling choices, experimental design, and conclusions.