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Basic discrete event simulation methodology: random number generators, generating random objects, design of discrete event simulation, validation, analysis of simulation output, variance reduction techniques, Markov chain Monte Carlo, simulation optimization. The course has computer assignments and projects. This course is a sequel to ISE/°¿¸éÌý760 Stochastic Models which serves as a prerequisite. This is NOT a software based course! Students who are looking for a class on simulation software, such as Arena and Simio, are recommended to take ±õ³§·¡Ìý562 (master-level simulation class).

Students should have completed a course on stochastic models (similar to ±õ³§·¡Ìý560 or ±õ³§·¡Ìý760) and have a working knowledge of a programming language (e.g., Python, Matlab, R, or others).

Typically offered in Fall and Spring