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1. Introduction to Modeling and
Simulation
Lecture 1

2. Learning Objectives
• Understand system and modeling
• Explore need of simulation
• Learn classification of systems
• Basics of system theory

3. What is a System?
• Collection of components working together
• Has input, process, output, feedback
• Examples: traffic, hospital, banking system

4. System Analysis
• Identify purpose of system
• Define components and boundaries
• Understand environment

5. Why Model a System?
• Real systems can be complex or costly
• Models are safe and cost-effective
• Allow repeatable experiments

6. What is a Model?
• Simplified representation of reality
• Examples: map, equation, dummy patient

7. What is Simulation?
• Running a model to mimic real system
behavior
• Helps in prediction and optimization
• Examples: weather forecast, traffic design

8. System → Model → Simulation
• System = Real world
• Model = Simplified version
• Simulation = Execution of model

9. Classification of Systems
• Natural vs. Man-made
• Deterministic vs. Stochastic
• Static vs. Dynamic
• Continuous vs. Discrete
• Open vs. Closed

10. System Theory Basics
• Every system has:
• Input → Process → Output → Feedback
• Example: Air conditioner with thermostat

11. Advantages of Simulation
• Cost-effective
• Risk-free experiments
• Time-saving
• Repeatable

12. Limitations of Simulation
• Not 100% accurate
• Requires valid data
• Can be computationally expensive

13. Applications
• Engineering (aircraft, cars)
• Medical (disease, surgery training)
• Business (bank queues, supply chain)
• Computer Science (networks)

14. Summary
• System → Model → Simulation
• Types of systems
• Importance of system theory
• Applications of simulation

15. Review Questions
• Define system, model, and simulation
• Differentiate deterministic and stochastic
systems
• Give one real-life application of simulation