Contents |
Course Code
IAM 556 (9700556)
Credit
(3-0) 3
Prerequisites
Basic knowledge of Statistics & programming
Content/ Aims
The objective of this course is to introduce students an experimental problem solving methodology, which is extensively utilized when the problem to be solved is too complex or does not have any analytical solution.
In this course, first the discrete-event system simulation is introduced. Second, mathematical and statistical models are presented. Then, random variate generation thecniques are taught. Next, methods for the analysis of input data and simulation output are provided. Finally, appications of advanced topics such as metamodeling techniques and response surface methodology (RSM) in simulation are presented. In addition, the simulation software ARENA/SIMAN is introduced.
The students should complete a simulation project by applying the concepts taught in the course to a real-life problem.
Learning Outcomes
Students taking this course will be equipped with the use of the tools and techniques of statistical simulation to be able to solve especially complex real-world problems.
Suggested Textbooks
- A.M. Law & W.D. Kelton, Simulation, Modeling and Analysis, Mc Graw Hill, NY, 2001.
- Jerry Banks, John S. Carson, Barry L. Nelson & David M. Nichol, Discrete-Event System Simulation, Prentice Hall, NJ, 2001.
- W.D.Kelton, R.P. Sadowski & D.A. Sadowski, Simulation with ARENA, Mc Graw-Hill, Boston, 2002.
- C.D. Pegden, R.E. Shannon & R.P.Sadowski, Introduction to Simulation Using Siman, Mc Graw-Hill, 1995
Outline
- Introduction to Simulation & Simulation Software:ARENA/SIMAN
- Statistical Models in Simulation
- Queuing Models
- Random Variate Generation
- Input Modeling
- Verification & Validation of Simulation Models
- Output Analysis & Evaluation of Alternative System Designs
- Variance Reduction Techniques
- Simulation Metamodelling
- The Use of Experimental Design & Optmization (RSM) tecniques on Simulation Models