1. Chapter 1

2. CS 487 Simulation and Modeling
What is Simulation?
A simulation is the imitation of the operation of a real-world process or system over time.
Could be done by hand or on a computer.
Involves generation of data & artificial history of a system, observation of the data and history, and inferences concerning the system’s characteristics.
To study a system, we often have to make assumptions about the operation of the system.
Assumptions constitute a model, used to understand the behavior of a system.
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

3. CS 487 Simulation and Modeling
What is Simulation?
Analytical solutions: if the model relationships are simple enough to use mathematical methods to obtain exact information on system
Simulation: Develop a simulation model and evaluate the model usually with a computer to estimate the desired characteristics of the model.
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

4. CS 487 Simulation and Modeling
Simulation Model
A simplified representation of a sys. (or process or theory), not sys. Itself.
Models can’t have all attributes; they are simplified, controlled, generalized, or idealized.
For a model to be useful, all its relevant behaviors must be determined in a practical way, given a reasonably limited set of descriptions.
A model must be validated.
After validation, a model can be used to investigate and predicate system behaviors, or answer “what-if” questions to enhance understanding, training, prediction, and evaluation of alternatives.
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

5. When Simulation is Appropriate?
Study the internal interactions of a complex (sub)-system.
Observe model’s behavior and resulting outputs due to changes on external environment or internal variables.
Improve system through model building.
Experiment new designs and policies prior to implementation.
Understand & verify analytic solutions.
Identify & determine requirements.
Allow training & learning at a lower cost.
Visualize operations through animation.
It’s difficult, time-consuming, expensive, hazardous, or impossible to solve the problem by conventional analytic or numeric methods.
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

6. When Simulation is Not Appropriate?
When the problem can be solved using common sense.
When the problem can be solved analytically.
When it is easier to perform direct experiments.
When the costs exceed the savings.
When resources or time are not available.
When no data is available.
When verification & validation cannot be performed.
When the power is overestimated.
When the system is too complex or can’t be defined.
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

7. Advantages of simulation
New policies, operating procedures, information flows and son on can be explored without disrupting ongoing operation of the real system.
New hardware designs, physical layouts, transportation systems and … can be tested without committing resources for their acquisition.
Time can be compressed or expanded to allow for a speed-up or slow-down of the phenomenon( clock is self-control).
Insight can be obtained about interaction of variables and important variables to the performance.
Bottleneck analysis can be performed to discover where work in process, the system is delayed.
A simulation study can help in understanding how the system operates.
“What if” questions can be answered.
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

8. Disadvantages of simulation
Model building requires special training.
Vendors of simulation software have been actively developing packages that contain models that only need input (templates).
Simulation results can be difficult to interpret.
Simulation modeling and analysis can be time consuming and expensive.
Many simulation software have output-analysis.
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

9. CS 487 Simulation and Modeling
Areas of Applications
Computer systems, e.g. scheduling, memory management
Computer networks and communications
Manufacturing
Semiconductor manufacturing
Construction engineering
Military applications
Transportation
Business process
…..
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

10. Systems and system environment
To model a system, we first need to know what a system is.
A system: objects and their relationships and interactions.
System environment: changes happen outside the system, but affect the system
Boundary
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

11. CS 487 Simulation and Modeling
Components of a System
Entity: system object of interest (e.g. Customers in a bank, machines in a factory)
Attribute: property of an entity (e.g. Customers account, machines speed/capacity)
Activity: operation in a specific time period (e.g. customers withdraw, machines welding)
State: collection of variables needed to describe the system at any time (e.g. #customers waiting, busy/idle machine)
Event: an instantaneous occurrence that may change the state of the system (e.g. customer arrivals, machine breakdown).
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

12. CS 487 Simulation and Modeling
Components of a System
Partial components shown in the table
Full components are based on the simulation objectives.
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

13. CS 487 Simulation and Modeling
Components of a System
Specify the system components for
A cafeteria
A grocery store
An automobile assembly line
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

14. Discrete and Continuous Systems
Discrete system: state variable(s) change only at a discrete set of points in time.
Example: number of customers waiting in line
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

15. Discrete and Continuous Systems
Continuous system: state variable(s) change continuously or smoothly over time.
Example: chemical level in a tank, electric current
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

16. CS 487 Simulation and Modeling
Model of a System
To study the system
it is sometimes possible to experiments with system
This is not always possible (bank, factory,…)
A new system may not yet exist
Model: construct a conceptual framework that describes a system
It is necessary to consider those accepts of systems that affect the problem under investigation (unnecessary details must remove)
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

17. CS 487 Simulation and Modeling
Types of Models
Models:
Physical: model home, model of a bridge, wax model of a person
Mathematical (symbolic): a(j!skf)(nvoe) = me3!
Simulation model
Static (at some point in time) vs. Dynamic (change over time)
Deterministic (known inputs) vs. Stochastic (random variables, inputs/outputs)
Discrete vs. Continuous
Focus of this course:
Discrete, dynamic, and stochastic
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

18. CS 487 Simulation and Modeling
Types of Models
Known inputs
Random inputs
At given time
Change over time
Chemical levels in a tank with known input rate
Arrival to doctor office with appointments
Arrival to a cafeteria
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

19. Discrete-Event System Simulation
Modeling of systems in which the state variable changes at a discrete set of points in time.
Methods: numerical instead of analytical
Analytical: deductive reasoning of math; accurate
Numerical: computational procedures; approximate
Simulation models are “run” rather than solved.
Observation of the real system, entities, interactions
Assumptions of the model
Collection of data
Analysis and estimation of system performance
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

20. CS 487 Simulation and Modeling
Three Model Levels
Conceptual
Very high level
How comprehensive should the model be?
What are the state variables, which are dynamic, and which are important?
Specification
On paper
May involve equations, pseudocode, etc.
How will the model receive input?
Computational
A computer program
General-purpose PL or simulation language?
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

21. Steps in a Simulation Study
Figure 1.3:
Problem formulation
Setting of objectives and project plan
Model conceptualization
Data collection
Model translation
Verification
Validation
Experimental design
Production runs and analysis
More runs
Documentation and reporting
Implementation
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

22. CS 487 Simulation and Modeling
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

23. CS 487 Simulation and Modeling
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

24. Steps in a Simulation Study
The previous steps can be classified in phases as follows:
Phase 1: Discovery and orientation
Fine-tune, recalibration, clarifications
Problem formulation
Setting of objectives and project plan
Phase 2: Model building and data collection
Continuing interplay,
Model conceptualization
Data collection
Model translation
Verification
Validation
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

25. Steps in a Simulation Study
Phase 3: Running the model
carefully organized plane to experiment the simulation model, statistical experiment involves estimated output with errors
Experimental design
Production runs and analysis
More runs
Phase 4: Implementation
Documentation and reporting
Implementation
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

26. Steps in a Simulation Study
Problem formulation
Analyst and policy maker should agree and understand the formulation
Setting of objectives and project plan
Questions to be answered. Appropriate to do simulation: as formulated or as objectives stated?
Alternative systems to be used and how to be evaluated? Peoples involved? Time required? Cost? Anticipated results?
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

27. Steps in a Simulation Study
Model conceptualization
Map the essence of the real system to the model
Data collection
Influenced by the objectives
Data to be able to do simulation and obtain results
Data to be used for validation of the simulation
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

28. Steps in a Simulation Study
Model translation
Written as a code or in a form that can be assessed, calculated or evaluated
Verification
Is the model built right? is the computer program performing properly? Need debug?
Validation
Compare the simulation model to the actual system? Iterative until acceptable accuracy level achieved.
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

29. Steps in a Simulation Study
Experimental design
Expected runs, alternatives to be tested, parameters to be used?
Production runs and analysis
Estimate the measures of performance.
More runs
Documentation and reporting
Program and progress
Implementation
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

30. Verification and Validation
The most important step in the process: validation
Validation should not be an isolated task that follows model development, but rather an integral part of model development.
Verification: “Are we building the model right?”
Is the model programmed correctly (input parameters and logical structure)?
Validation: “Are we building the right model?”
Is the model an accurate representation of the real system?
Iterative process of comparing the model to actual system behavior and refine the model
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

31. CS 487 Simulation and Modeling
Model Building
Iterative process consisting of three main steps:
Observe the real system & the interactions of components and collect data
Domain specific knowledge
Stakeholders: operators, technicians, engineers, …
Construct a conceptual model
Assumptions or hypotheses on components and parameter values
Structure of the system
Translate the operational model to computer recognizable form
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008

32. Uses of PCs in simulation steps:
Data Collection (step 4) –
Storage of raw data in a file would allow rapid accessibility and a large memory at a very low cost.
The data could be easily augmented as it is being collected.
Analysis of the data could also be performed using currently available software.
Model Translation (step 5) –
Many simulation languages are now available (see Chapter 4).
Validation (step 7) –
Validation is partially a statistical exercise. Statistical packages are available for this purpose.
Experimental Design (step 3) - Same response as for step 7.
Production Runs (step 9) - See discussion of step 5 above.
Documentation and Reporting (step 11) –
Software is available for documentation assistance and for report preparation.
Jordan University of Science and Technology – Computer Science Department
CS 487 Simulation and Modeling
First semester 2007/2008