The course gives the theoretic aspects of simulation, followed by its probabilistic and statistical un-derpinnings, including random number generation. It addresses simulation-related theory of input analysis, and output analysis. It also provides a background about Markov chain processes and queu-ing theory. Finally, the course describes and illustrates modeling of some applications using simula-tion software.
Software Engineering Plan -2021
Jerry Banks, John Carson, Barry Nelson, and David Nicol, Discrete-Event System Simulation, Pearson
content serial | Description |
---|
1 | Introduction to Simulation |
2 | Steps in Simulation Study |
3 | Monte Carlo Simulation |
4 | Discrete Event Simulation |
5 | Statistical Models in Simulation |
6 | Statistical Models in Simulation (cont.) |
7 | 7th Week Exam |
8 | Random-Number Generation |
9 | Random-Variate Generation |
10 | Input Modeling |
11 | Output Analysis |
12 | 12th Week Exam |
13 | Markov Chain |
14 | Queuing Models |
15 | Revision |
16 | Final Exam |
Start your application