|System Modeling and Simulation
|The course gives the theoretic aspects of simulation, followed by its probabilistic and statistical underpinnings, including random number generation. It addresses simulation-related theory of input analysis, output analysis, verification and validation. It also describes and illustrates modeling of queuing systems using Markov chains. Different Industrial applications are modeled and simulated using simulation software.
|Upon completion of this course, students should be able to: 1. Understand the power and limitations of discrete event simulation.2. Recognize alternative discrete event Simulation models.3. Plan for and Design a simulation experiment.4. Apply simulation models to alternative systems and problems.5. Apply standard statistical techniques in analyzing the output data from a simulation experiments.6. Use the modeling of queuing systems using Markov chains.7. Evaluate performance of queuing systems.
|Barry L. Nelson, Stochastic Modeling: Analysis and Simulation, McGraw - Hill, 1995.
|arabic ref. books
|Banks J. and Carson J. II, Discrete-Event System Simulation, 5th Edition, Pearson, 2009.rn