1. CS 450 Modeling and Simulation
Dr. X
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2. Topics Modelling vs Simulation Applications of Simulation Why Simulate
How to Simulate
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3. What Is A Model ?
A Representation of an object, a system, or an idea in some form other than that of the entity itself. (Shannon)
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4. A description of the system you wish to study
What Is A Model ?
A description of the system you wish to study
What is a system? A collection of elements that act to achieve some goal
Does the model always have to be real? Vampires in NY City
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5. Types of Models Physical Mathematical Computational Scale models,
prototype plants,…
Mathematical
Analytical queueing models,
linear programs,
Simulation
Computational
Weather forecasting
Earth Simulator
Flight, Combat Simulator
Molecular simulator
Nuclear simulator
A computational model is a mathematical model in computational science that requires extensive computational resources to study the behavior of a complex system by computer simulation. The system under study is often a complex nonlinear system for which simple, intuitive analytical solutions are not readily available. Rather than deriving a mathematical analytical solution to the problem, experimentation with the model is done by adjusting the parameters of the system in the computer, and studying the differences in the outcome of the experiments. Operation theories of the model can be derived/deduced from these computational experiments.
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6. Why do we need models?
To simulate, to make assumptions, we cannot simulate every detail in a system,
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7. Simulation Models
1. Environment 2. Interdependency 3. Sub-systems 4. Organization 5. Change
1. Environment: Each system can be seen as a subsystem of a broader system.
2. Interdependency: No activity takes place in total isolation.
3. Sub-systems: Each system can be broken down to sub-systems.
4. Organization: Virtually all systems consist of highly organized elements or
components, which interact in order to carry out the function of the system.
5. Change: The present condition or state of the system usually varies over a long
period of time.
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8. Beard’s Managerial Pyramid
Level of detail
Beard’s Managerial Pyramid
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9. Implementation of a model
What is Simulation?
Implementation of a model
How do we computer scientists implement? With programs and algorithms
What is a program? Implementation of an algorithm
What is an algorithm? Precise set of instructions for accomplishing a task
A simulation is to models what a program is to algorithms!!
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10. Why simulate? Cost Safety Speed Try before implementing Creativity
Detailed design
Predictions
Experimentation
Are there any disadvantages? Expensive, time consuming, not accurate, tough to validate, it is not a Gospel!
When everything fails, simulate!
Find bugs, PoC
REDUCE RISK!
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11. Simulation Applications
Designing and analyzing manufacturing systems
Evaluating H/W and S/W requirements for a computer system
Evaluating a new military weapons system or tactics
Determining ordering policies for an inventory system
Designing communications systems and message protocols for them
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12. Simulation Applications
Designing and operating transportation facilities such as freeways, airports, subways, or ports
Evaluating designs for service organizations such as hospitals, post offices, or fast-food restaurants
Analyzing financial or economic systems
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13. Steps In Simulation and Model Building
1. Define an achievable goal 2. Put together a complete mix of skills on the team 3. Involve the end-user 4. Choose the appropriate simulation tools 5. Model the appropriate level(s) of detail 6. Start early to collect the necessary input data
1-3 very similar to any software project
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14. Steps In Simulation and Model Building(cont’d)
7. Provide adequate and on-going documentation 8. Develop a plan for adequate model verification (Did we get the “right answers ?”) 9. Develop a plan for model validation (Did we ask the “right questions ?”) 10. Develop a plan for statistical output analysis
7 also classic in sw projects
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15. Define An Achievable Goal
“To model the…” is NOT a goal!
“To model the…in order to select/determine feasibility/…” is a goal.
Goal selection is not cast in concrete
Goals change with increasing insight
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16. Put together a complete mix of skills on the team
We Need:
Knowledge of the system under investigation
System analyst skills (model formulation)
Model building skills (model Programming)
Data collection skills
Statistical skills (input data representation)
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17. Put together a complete mix of skills on the team(continued)
We Need:
More statistical skills (output data analysis)
Even more statistical skills (design of experiments)
Management skills (to get everyone pulling in the same direction)
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18. Involve the end user Modeling is a selling job!
Does anyone believe the results?
Will anyone put the results into action?
The End-user (your customer) can (and must) do all of the above BUT, first he must be convinced!
He must believe it is HIS Model!
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19. Choose The Appropriate Simulation Tools
Build Model in a General Purpose Language
Build Model in a General Simulation Language
Use a Special Purpose Simulation Package
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20. Modelling w/ general purpose languages
Advantages:
Little or no additional software cost
Maximum customization
Universally available (portable)
No additional training (Everybody knows…(language X) ! )
Disadvantages:
Every model starts from scratch
Very little reusable code
Long development cycle for each model
Difficult verification phase
Reusable code: it depends how well it is designed
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21. Gen. Purpose languages used for simulation
FORTRAN
Probably more models than any other language.
C++, Java, C#
Object-oriented programming language
Python, Perl, Ruby
Interpreted programming languages
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22. Modeling w/ general simulation languages
Advantages:
Standardized features often needed in modeling
Shorter development cycle for each model
Much assistance in model verification
Very readable code
Disadvantages:
Higher software cost (up-front)
Additional training required
Limited portability
Limited customization
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23. General purpose simulation languages
SimPy
Arena
NS3
Csim
ExtendSim
Simio
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24. Special purpose packages used for simulation
OPNET
Simulator for communication networks, including wireless networks
Modelica
Matlab Simulink
Other
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25. TERMINOLOGY
System
A group of objects that are joined together in some regular interaction or interdependence toward the accomplishment of some purpose.
Entity
An object of interest in the system.
E.g., customers at a bank
Analygy with program, OO programming
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26. TERMINOLOGY (continued)
Attribute
a property of an entity
E.g., checking account balance
Activity
Represents a time period of specified length.
Collection of operations that transform the state of an entity
E.g., making bank deposits
Attributes/characteristics of objects in OO programming
Activity: method
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27. TERMINOLOGY (continued)
Event:
change in the system state.
E.g., arrival; beginning of a new execution; departure
State Variables
Define the state of the system
Can restart simulation from state variables
E.g., length of the job queue.
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28. TERMINOLOGY (continued)
Process
Sequence of events ordered on time
Note:
the three concepts(event, process,and activity) give rise to three alternative ways of building discrete simulation models
Process- main?
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29. EXAMPLES OF SYSTEMS AND COMPONENTS
Note: State Variables may change continuously (continuous sys.)
over time or they may change only at a discrete set of points
(discrete sys.) in time.
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30. SIMULATION “WORLD-VIEWS”
Pure Continuous Simulation
Pure Discrete Simulation
Event-oriented
Activity-oriented
Process-oriented
Combined Discrete / Continuous Simulation
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31. Examples Of Both Type Models
Continuous Time and Discrete Time Models:
CPU scheduling model vs. number of students attending the class.
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32. Other Models Deterministic and Probabilistic Models:
Input
Output
Static and Dynamic Models:
CPU scheduling model vs. E = mc2
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33. START EARLY TO COLLECT THE NECESSARY INPUT DATA
Data comes in two quantities:
TOO MUCH!!
TOO LITTLE!!
What are the issues?
With too much data, we need techniques for reducing it to a form usable in our model.
With too little data, we need information which can be represented by statistical distributions.
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34. PROVIDE ADEQUATE AND ON-GOING DOCUMENTATION
In general, programmers hate to document. (They love to program!)
Documentation is always their lowest priority item. (Usually scheduled for just after the budget runs out!)
They believe that “only wimps read manuals.”
What can we do?
Use self-documenting languages
Insist on built-in user instructions(help screens)
Set (or insist on) standards for coding style
Do you see your self in any of these bullets?
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35. DEVELOP PLAN FOR ADEQUATE MODEL VERIFICATION
Did we get the “right answers?”
(No such thing!!)
Simulation provides something that no other technique does:
Step by step tracing of the model execution.
This provides a very natural way of checking the internal consistency of the model.
Like debugging and using the debugger to step in
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36. DEVELOP A PLAN FOR MODEL VALIDATION
VALIDATION: “Doing the right thing” Or “Asking the right questions”
How do we know our model represents the system under investigation?
Compare to existing system?
Deterministic Case?
Mathematical model?
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37. DEVELOP A PLAN FOR STATISTICAL OUTPUT ANALYSIS
How much is enough?
Long runs versus Replications
Techniques for Analysis
Take some statistics classes!
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38. References
The Guide to Computer Simulations and Games, By Katrin Becker and J.R.Parker, Wiley (Chapter 2)
Simulation Modeling and Analysis, By Law and Kelton, McGrawHill, (Chapter 1)
Computer Simulation Techniques: The Definitive Introduction, by Harry Perros, (Chapter 1)
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