Basic Information
We Will Study Theoretical and Applied Aspects of Simulation, In Particular Monte Carlo Simulation and Discrete Event Simulation. The Course Will Include Three Main Parts. The First Part Will Focus On Generating Random Variables Using The Inversion, Acceptance-rejection And Composition Methods. We Will Learn How to Generate Non-correlated And Correlated Normal Variables As Well As Homogenous And Non-homogeneous Poisson Processes. The Second Part Will Be Devoted To Discrete Event Simulation, and Will Include Simulation of Queueing For a Variety of Systems Such As Service, Healthcare, Transportation, Systems, Markovian Chains and Inventory Systems. We Will Learn How To Construct and Analyze a Discrete Event Simulation Model in Pythonsim Logistic, Distribution and Project Management. The Third Part of The Course Will Focus On Variance Reduction Techniques and Importance Sampling.
Faculty: Data and Decision Sciences
|Undergraduate Studies
Pre-required courses
(94314 - Stochastic Models in Oper.research and 94423 - Introduction to Statistics) or (94314 - Stochastic Models in Oper.research and 94424 - Statistics 1)
Course with no extra credit
48986 - Stochastic Methods and Models 94334 - Simulation-modeling and Analysis