1 Modeling and Simulation

Modeling and Simulation

The main objective of this course is to provide an insight into how Modeling and simulation can aid in analyzing, developing and solving a real problems. Modeling is the process of representing a complex system by a model. Computer Simulation is the process of designing a model of a system and conducting experiments to understand the behavior of the system and/or evaluate various alternatives on its operation. Modeling & Simulation (M&S) has become an important tool in all phases of the acquisition process and can be used within most systems life cycle processes. M&S represents a Discipline of understanding and evaluating the interaction of parts of a real or theoretical system by; Designing its representation (model ) and Executing (running) the model. An approach of using object modeling during systems analysis and design is called object-oriented analysis.
This subject provides the students with an ability to:
• Describe fundamental of the systems and models.
• Understand the Modeling Process of the systems.
• Examine the basic system concepts and definitions
• Learn about the techniques to model and to simulate various systems;
• Analyze a system to make use of the information to improve its performance.
• Explain how models and simulations are used in each step of the acquisition process.
• Recognize when M&S will provide meaningful support to a technical program.
• Understand how to plan for effective M&S.
• Understand the range of simulation methodologies and areas for which they are most applicable.
• Examine foundational technologies, including design of experiments, system modeling and optimization.
• Examine simulation design issues, including how parallel and distributed simulation standards can be used to support system of system designs.

1. Introduction to Systems, models and modeling process.
2. Definitions of Computer Simulations with applications.
3. Random Numbers generation.
4. Random Variables (discrete R.V and Continuous R.V)
5. Generating Random Variates ( Inverse Transform and Rejection Technique)
6. Generating random variates from empirical data.
7. Generating random variates from different probability distributions.
8. Queuing Models with applications.
9. Discrete Event Simulation with examples
10. Monte-Carlo Simulation
11. Modeling and Simulation problems