System Modeling and Simulation

  • Software Engineering |

Description

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.

Program

Software Engineering 144 CRs

Objectives

  • 1. Understand the basic principles of the field of Modeling and Simulation.
    2. Apply standard statistical techniques in analyzing input data for a simulation experiment.
    3. Use Markov chains theory for modeling of queuing systems.
    4. Plan for and design a simulation experiment for some problems
    5. Evaluate performance of queuing systems.

Textbook

Jerry Banks, John Carson, Barry Nelson, and David Nicol, Discrete-Event System Simulation, Pearson

Course Content

content serial Description
1Introduction to Simulation
2Steps in Simulation Study
3Monte Carlo Simulation
4Discrete Event Simulation
5Statistical Models in Simulation
6Statistical Models in Simulation (cont.)
77th Week Exam
8Random-Number Generation
9Random-Variate Generation
10Input Modeling
11Output Analysis
1212th Week Exam
13Markov Chain
14Queuing Models
15Revision
16Final Exam

Markets and Career

  • Generation, transmission, distribution and utilization of electrical power for public and private sectors to secure both continuous and emergency demands.
  • Electrical power feeding for civil and military marine and aviation utilities.
  • Electrical works in construction engineering.

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