A first graduate course covering the theory and practice of discrete-event stochastic simulation. Coverage includes Monte Carlo methods and spreadsheet applications, generating random numbers and variates, specifying input probability distributions, discrete-event simulation logic and computational issues, review of basic queueing theory, analysis of correlated output sequences, model verification and validation, experiment design and comparison of simulated systems, and simulation optimization. Emphasis includes state-of-the-art simulation programming languages with animation on personal computers. Applications address operations in manufacturing, distribution, transportation, communication, computer, health care, and service systems.
Discrete Event Stochastic Simulation
Host University
University of Virginia
Semester
Spring 2024
Course Number
SYS 6034-600
CRN
16589
Credits
3
Discipline
Systems Engineering, Operations Research and Engineering Management
Instructor
Brian Park
Course Information
Prerequisites
SYS 6005 or equivalent background in probability, statistics, and stochastic processes