Simulation and Modelling-SE
Page 1: Course Introduction
Course: Simulation and Modelling Techniques
Program: Software Engineering Program, Computer Sciences
Semester: 5th Semester, 3rd Year
Instructor: Engr. Mehran M. Memon
Page 2: Learning Objectives
At the end of this lecture, students will learn to:
Understand systems and models.
Understand the concept of computer simulation.
Recognize the importance of simulation as an analysis tool.
Identify the various types of computer simulations.
Page 3: Understanding Systems
System: Comprises components and their environment.
Components: Parts or elements that make up the system.
Environment: External factors impacting the system.
Inputs: Resources or data fed into the system.
Outputs: Results produced by the system.
System Boundary: Defines the limits separating the system from its environment.
Page 4: Examples of Systems
Freeway system
Manufacturing facility
Business processes (e.g., insurance office)
Bank operations
Criminal justice system
Airport operations
Chemical plants
Transportation and logistics
Fast-food restaurant operations
Hospital facilities
Emergency-response systems
Computer networks
Page 5: System Environment and Boundaries
Changes affecting a system occur in its environment.
It's essential to define system boundaries to distinguish between influenced elements and external factors.
Example: In a factory system, order arrival may be outside the factory's control, while supply and demand are internal variables affecting output.
Page 6: Components of a System
Entity: Flow units transformed over time.
Attribute: Property of an entity (e.g., a bank's account balance).
Activity: Defined time period for operations (e.g., making deposits).
Page 7: State of a System
State of a System: Collection of variables describing its condition concerning study objectives.
Example for a bank:
Number of busy tellers
Number of waiting customers
Arrival times of customers
Page 8: Endogenous vs. Exogenous Events
Endogenous: Events occurring within a system.
Exogenous: Environmental events affecting the system.
Example:
Customer arrival at the bank (exogenous)
Service completion (endogenous)
Page 9: Components of a System Review
Entities: Customers, checking accounts
Attributes: Account balances
Activities: Deposits, arrivals, departures
State Variables: Number of busy tellers, customers in queue
Page 10: Summary of System Components
Entities, attributes, activities, events, and state variables are necessary for analyzing different systems like banking and rapid transit.
Page 11: Types of Systems
Continuous Systems: Change continuously over time.
Discrete Event Systems: Change at distinct points in time.
Page 12: Understanding Discrete Systems
Discrete System Example: Banking, where variables like customer count change only on customer arrival or service completion.
Page 13: Understanding Continuous Systems
Continuous System Example: Water behind a dam changes level continuously over time.
Page 14: Studying Systems
Methods for studying systems include experimentation with the actual system or its model.
Page 15: System Study Methods
Physical (iconic) models
Mathematical models
Analytical solutions
Simulation techniques
Page 16: Comprehensive Study Methods
Simulations help address complexities and provide insights into system behavior.
Page 17: Models of a System
Real System: Actual operations
Model: Simplifications and assumptions that represent the system.
Page 18: System Modeling Elements
Analytical Model: Provides optimal solutions.
Simulation Model: Captures complexities and dynamics of real systems.
Page 19: Logical Models in Analysis
Traditional mathematical methods work for simple models but can fail in complexities.
Over-simplification risks yielding an invalid model.
Page 20: General Definition of Simulation
Simulation involves modeling systems to analyze behavior and performance under various conditions.
Page 21: Key Terms in Simulation
Model: Logical/mathematical abstraction of the system.
Input Variables: Parameters defining the system.
Output Variables: Performance measures.
Experiment: Generating instances to estimate performance.
Page 22: Performance Measures
Metrics relevant to system performance derived from simulations.
Page 23: Importance of Simulation
Systems exhibit complexity with interdependent objects, randomness, and variations affecting operations.
Page 24: Emergency Department Scenario
Patient dynamics in emergency departments illustrate the necessity for simulations to analyze performance and improve operations.
Page 25: Performance Metrics in Emergencies
Measures: Average wait time, patient throughput, rooms required, staff utilization.
Page 26: Use of Historical Data
Historical records inform on operational performance and guide future improvements in hospital systems.
Page 27: Modeling Constraints and Techniques
Real system experimentation can be harmful; therefore, modeling is preferred to predict changes in system behavior.
Page 28: Total System Analysis Capability
Simulation modeling effectively addresses whole system dynamics, overcoming limitations of traditional models.
Page 29: Simulation Appropriateness
Opt for simulation when models are too complex for analytical solutions. Established systems lend to statistical estimation rather than exact figures.
Page 30: Computer Simulation Definition
Computer simulation is a numerical technique to conduct experiments on digital platforms that describes system behavior over time.
Page 31: Simulation Types
Deterministic vs. Stochastic: Certainty vs. uncertainty.
Static vs. Dynamic: Time's influence on the model.
Continuous vs. Discrete: State changes over time versus at specific events.
Page 32: Discrete-Event Simulation Defined
Observations at specific times when system changes occur (e.g., customer transactions in fast food).
Page 33: Continuous Simulation Defined
Requires full-time observations, modeling fluid dynamics and changes continuously over time (e.g., oil levels in tankers).
Page 34: Combined Modeling Techniques
Systems can simultaneously involve discrete and continuous aspects, requiring multiple modeling approaches.
Page 35: Advantages of Simulation
Models reality flexibly and accommodates complex systems without oversimplification.
Page 36: Flexibility in Simulation
Avoids bias or blind spots in analysis, like looking where it's easier rather than where issues truly lie.
Page 37: Simulation Advancements
Increased computing power, user-friendly software, and statistical capabilities greatly enhance simulation utility.
Page 38: Disadvantages of Simulation
Simulation provides estimates rather than definitive answers; model building requires expertise and can be time-consuming.
Page 39: Conclusion
Understanding both the benefits and drawbacks of simulation is critical in applying it appropriately to system analysis.