DCS307

This course introduces topics in computer simulation, focusing on the underlying theory, implementation, and analysis of discrete-event simulation models. Topics include discrete-event simulation, Monte Carlo simulation, random number generation, discrete and continuous stochastic models, input modeling, statistics and visualization for output analysis, and point and interval parameter estimation in simulation contexts. The course focuses heavily on real-world systems that are appropriately modeled using queuing and agent-based simulation models. The course is simultaneously theoretical and computational. Students use mathematical and statistical derivations, as well as existing software libraries in R and Python, to understand and analyze simulation models. Software development is also a significant component of the course, as students work in teams to design, implement, and analyze the results of their own models. Prerequisite(s): DCS 211 or 229.

Students should have at least one 100-level programming course and a subsequent 200-level course in which software development plays a significant role, even if not the primary focus.