Senior and graduate students who need to add another course to their spring schedules are invited to enroll in ELEC 498/898: Computational Modeling and Simulation I: Discrete Systems.
In Collaboration with Technical University of Clausthal, Germany
Wednesday and Fridays: 8:30-10 a.m.
Goal: To introduce the fundamental concepts of computational modeling and simulation of discrete systems and their applications, ranging from description and development of a model to simulation and analysis of their results using simulation software. Modeling techniques, including Monte Carlo Simulation, and verification and validation, analysis in the context of a design will be covered.
Prerequisites: A course in Probability and/or statistics, a course in high level programming language
Course outline:
· Intro to discrete-event simulation: Areas of application; system environment; components of a system; model of a system; types of model
· Simulation Examples: How to simulate randomness; queuing simulation with single and multiple servers; estimating the distribution of lead-time demand
· Concepts in discrete-event simulation; event scheduling; time advance algorithm; dynamic allocation
· Review of basic probability and statistics concepts PMF, CDF, central limit theorem, laws of large numbers, expectation, variance, probability distribution functions
· Random number generation: Linear congruential method; tests for randomness; random variable generation
· Mathematical and Statistical Models: Simulation output data and stochastic processes; estimation of mean, variances and correlation; confidence interval and hypothesis testing
· Queuing models: Characteristics of queuing systems; queuing notation; single and multiple server queues; steady state behavior of infinite and finite population models; networks of queues
· Analysis of simulation data: Input modeling; data collection; sample mean; sample variance
· Monte Carlo Simulation and its application
· Verification and Validation of Simulation Models: Validation of model assumptions; validation of input-output transformation
· Sensitivity analysis of model parameters
· Case studies
For more info and registration, please contact: Dr. Vakilzadian (hvakilzadian@unl.edu)