1. Discrete Event Simulation with ExtendSim Chapter 1
Introduction to Modeling and Simulation
© Copyright 2015 Dr. Jeffrey Strickland

2. © Copyright 2015 Dr. Jeffrey Strickland
NASA Ares I
NASA planned to use Ares I to launch Orion, the spacecraft intended for NASA human spaceflight missions after the Space Shuttle was retired in 2011.
However, the Constellation program, including Ares I was canceled in October 2010 by the passage of the 2010 NASA authorization bill.
Dr. Strickland and a team of engineers design the Reliability and Launch Availability of the Ares I using ExtendSim.
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© Copyright 2015 Dr. Jeffrey Strickland

3. Why simulation is important
Simulation involves designing a model of a system and carrying out experiments on it as it progresses through time. Models enable you to see how a real-world activity will perform under different conditions and test various hypotheses at a fraction of the cost of performing the actual activity. One of the principal benefits of a model is that you can begin with a simple approximation of a process and gradually refine the model as your understanding of the process improves. This “stepwise refinement” enables you to achieve good approximations of very complex problems surprisingly quickly. As you add refinements, the model more closely imitates the real-life process1 1 ExtendSim user’s guide
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4. © Copyright 2015 Dr. Jeffrey Strickland
What is a Model?
A physical, mathematical, or otherwise logical representation of a system, entity, phenomenon, or process
An abstraction of a real world problem, based on simplifying assumption.
Since a modeling is a representation, abstraction, or approximation of the “system” being modeled, we must understand that it is not an “exact” representation, i.e., we can’t model every aspect of the system.
Example: Weather Model
Many degrees of freedom (DOF) with a vast array of environmental factors
Simplify model with only a few of the most important factors (other non-representative factors represent the error in the model)
Example: Marketing Model
People have a propensity to buy due to demographics, online activity, visual stimulation, emotions, etc.
Model model based on demographics and online activity
In order to understand what it means to model a phenomena or process, we must first understand the term “model” and understand its limitation.
A model is a physical, mathematical, or otherwise logical representation of a system, entity, phenomenon, or process (DoDD ). A model can also be thought of as an abstraction of the real world, or an approximation of it. If you think about the problem of modeling a human being, or just the mind of a human, you can immediately see the limitations of modeling. We will use the term “system” to encompass systems, entities, phenomenon, or process. Since a modeling is a representation, abstraction, or approximation of the “system” being modeled, we must understand that it is not an “exact” representation, i.e., we can’t model every aspect of the system. First, we don’t know everything we need to know in order to model the system. We may not be able to define a process of the system with mathematical precision, or with heuristic algorithms, and many of the processes may not appear logical. Second, even if we were to know everything about the system, we may not have enough computing power to model every process, at least for complex systems, e.g., a human being, the earth’s ecosystem, etc.
Yet with its limitations, models a re a good way to gain understanding of how a system operates. ______ said, “All models are wrong; some models are useful.” They are wrong in that there is not a one-to-one mapping form the real system to the model; they are useful in that we can use them to understand the system, or at least certain aspects of the system. Even though we often use models to predict behavior, this is a dangerous process, and we must do it with caution.
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5. Model Definitions & Contrasts
Physical model
Mock-up model
Scale model
Iconic model
Fashion model
Symbolic Model
Narrative model
Graphical model
Tabular model
Software model
Mathematical model
Specifications:
30 GB HD
1 GB RAM
CD RW/DVD
There are many types of models that one can use when trying to represent a system. These can be divided into several classes, and for simplicity we will consider two classes: physical models and symbolic models. Physical models include mock-up models (e.g., a vehicle mock-up), scale models (e.g., a 1:48 scale aircraft model), Iconic model, natural model, or a fashion model (which represent what we all want to look like).
Symbolic models include narrative models, graphical models, tabular models, software models, and mathematical models. These models are not necessarily mutually exclusive. For example a tabular model might contain data derived from a mathematical model, or a mathematical model may be embedded in software code.
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6. Contrasting Model Examples
Deterministic (Static) models
Stochastic (Dynamic) models
Descriptive models
Predictive models
Discrete models
Continuous models
Models can also be classified by contrast, e.g., a conceptual model (flowchart, picture, UML diagram) versus a computational model (spreadsheet, computer software) or static models that do not use time as a primary input, versus dynamic models based on time.
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7. A Sampling of Model Examples
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8. A Sampling of Examples Accounts Receivable at Spring Mills
Scatterplot of Amount versus Days for All Customers: Data Model
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9. A Sampling of Examples Competitive Bidding by SciTools Incorporated
Decision Tree Model
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10. A Sampling of Examples
Confidence Level
95%
sample mean
1500
sample standard deviation
390
sample size 45
z value
1.9600
upper confidence limit
lower confidence limit
width of CI
Goal Seek for CI width
150.00
sample size needed
Analysis of Auditing Example: Parameter Model (Confidence Interval for a Mean)
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11. A Sampling of Examples Explaining Overhead Costs at Bendrix
Training Weeks (X)
# of completed Projects
(Y)
1.2
101
0.8 92
1.0
110
1.3
120
0.7 90 82 93
0.6 75
0.9 91
1.1
105
Data for new employees: Regression Model
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12. A Sampling of Examples Quarterly Sales at Intel
Time Series Plot of Quarterly Sales at Intel: Forecasting Model
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13. A Sampling of Examples Basic Combat Attrition Model
X(0) = Y(0) = 30
a = b = 0.14
Y(0) ↑ 60
b ↑ 0.16
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14. © Copyright 2015 Dr. Jeffrey Strickland
A Sampling of Examples Tracking Market Shares of Two Dominant Companies in the Iced Tea Market
Input for Iced Tea Example: Monte Carlo Simulation Model
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15. © Copyright 2015 Dr. Jeffrey Strickland
A Sampling of Examples Discrete Event Simulation of a Transportation System
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16. © Copyright 2015 Dr. Jeffrey Strickland
What is Simulation?
A simulation is the execution of a model over time ot other parameter.
Simulation involves designing a model of a system and carrying out experiments on it.
The purpose of these "what if” experiments is to determine how the real system performs and to predict the effect of changes to the system as time progresses.
For example, you use simulation to answer questions like:
Will this change to our process result in higher yields or quality?
How many people are required to maintain service at a specified level, for example, in a processing station?
Can we design this weapon system with fewer components and still maintain measures of performance, such as availability or lethality?
Does this force structure meet the capability requirement specified by the commander?
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17. Formal Definitions of Simulation
A formal definition of simulation is given by the Department of Defense Directive:
Definition 1: A method for implementing a model over time. Also, a technique for testing, analysis, or training in which real-world systems are used, or where real-world and conceptual systems are reproduced by a model.
Another definition is given by Winston & Albright, in Practical Management Science, 2001:
Definition 2: A simulation model is a computer model that imitates a real-life situation
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18. Different Kinds of Simulation
Monte Carlo Simulation
Estimate stochastic, static model quantities that are difficult to compute by exact computations. A scheme employing random numbers which is used for solving certain stochastic problems where the passage of time plays no substantive role. 1
Dynamic Simulation
Dynamic system simulations observe the behavior of the system models over time. The time advance mechanism used here include continuous, discrete time, and discrete event. 2
Differential Equation System Specification (DESS) 2a
Discrete Time System Specification (DTSS) 2b
Discrete Event System Specification (DEVS) 2b
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19. Discrete-Event Simulation
Estimate stochastic, dynamic, and discrete model outputs. A scheme for modeling a system as it evolves over time by a representation in which state variables change instantaneously at separate points in time.
In simple terms, DES describes how a system with discrete flow units or jobs evolves over time.
Technically, this means that a computer tracks how and when state variables, such as queue lengths and resource availability, change over time.
State variables change as the result of an event (or discrete event) occurring in the system.
A characteristic is that discrete-event models focus only on the time instances when these discrete events occur.
This feature allows for significant time compression because it allows the model to skip through all time segments between events when the state of the system remains unchanged.
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20. © Copyright 2015 Dr. Jeffrey Strickland
Bank Model
Hierarchical modeling
Statistics collection
Buttons
Notebooks
Ghost Connectors
Multiple random inputs
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21. © Copyright 2015 Dr. Jeffrey Strickland
Circuit Card Assembly
Multiple queues
Multiple services
Parallel servises
Serial services
Conveyer belts
Batching
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22. © Copyright 2015 Dr. Jeffrey Strickland
Transportation
Labor pools
Task completion delays
Transferring goods
Transporting goods
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23. Languages for Simulation
1950’s began seeking languages specifically designed for simulation problems
General Simulation Program (GSP) 1960
The First Simulation-specific Programming Language
By K.D. Tocher and D.G. Owen, General Electric
Proceedings of the Second International Conference on Operations Research
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24. Evolved Definition of Simulation Language
Six key characteristics:
Generate Random Numbers
Transformation for Statistical Distributions
List Processing
Statistical Analysis
Report Generation
Timing Execution
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25. Typical Simulation Program
Start
Set clock
Set state variables
Load event list
Initialization
0. Invoke Initialization
Invoke Timing
Invoke Event Handler
Main
Select next event
Advance sim clock
Timing
0. Update state variables
Increment counters
Generate future events
Event Routines
Statistical distributions
Mathematical operations
Mathematics
Finished? NO
Legend
Programmer’s
responsibility
Language’s
Responsibility
Compute interest data
Write reports
Reports
YES
Stop
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© Copyright 2015 Dr. Jeffrey Strickland

26. © Copyright 2015 Dr. Jeffrey Strickland
Activity
Scan
GSP
SIMPAC
CSL
ESP
ECSL
OPS-1,2
OPS-3
OPS-4
GPSS V6000
Process
Interaction
GPSS
GPSS II
GPSS III
GPSS/360
GPSS IV
GPSS/H
GPSS 85
GPSS PC
GPSS PL/I
SIMULA I
SIMULA 67
NGPSS
CONSUM
Event
Interaction
GASP
GASP II
GERTS
GASP IV
GASP PL/I
SLAM
SIMAN
SLAM II
GEMS
SPS-1
SIMSCRIPT
QUICKSCRIPT
SIMSCRIPT II
SIMSCRIPT II+
SIMSCRIPT II.5
SIMFACTORY II.5
MOSIM
NETWORK II.5
COMNET II
1960
1965
GSP (General Simulation Package) was developed by K.D. Tocher and D.G. Owen in 1959, at General Electric.
SIMPAC was an early simulation language with fixed time steps, developed R.P. Bennett et al, Systems Development Corp, Apr 1962.
OPS-1 (On-line Process Synthesizer) was an attempt to make a general purpose programming language that suited the needs of management. It was developed by Martin Greenberger, MIT.
The SIMULA programming language was designed and built by Ole-Johan Dahl and Kristen Nygaard at the Norwegian Computing Center (NCC) in Olslo between 1962 and It was originally designed and implemented as a language for discrete event simulation, but was later expended and reimplemented as a full scale general purpose programming language. SIMULA 67 is one of the key ancestors of the C++ language.
GPSS (General-Purpose Simulation System) is a process oriented simulation language, developed by IBM in 1961, and is well suited for queuing systems. IBM stopped enhancing GPSS in 1972.
GPSS/H was a compiled language developed in 1977 by James Henriksen and marketed by Wolverine Software. It filled the void left by not enhancing GPSS.
GPSS/PC was developed by Springer Cox in 1984 and marketed by Minuteman Software.
SIMSCRIPT is a process-oriented or event-oriented simulation language developed at the Rand Corporation in 1962, by Herbert Karr, Harry Markowitz, and Bernard Hausner. SIMSCRIPT II.5 is the latest version and is marketed by CICI Products Company. It is used in SimProcess. MODSIM is a general purpose object oriented programming language.
GASP (General Activity Simulation Program) was a dicrete event simulation system/language featuring flow-chart input and semi-object features, developed by Philip Kiviat, Applied Research Laboratory at United States Steel Corporation and Cornell U Postgrad. Began in 1961, initially based on Algol 60, then moved to Fortran II by 1962.
SLAM (Simulation Language for Alternative Modeling) was developed by Dennis Pegden and Alan Pritsker in 1979 and was distributed by the Pritsker Corporation. It is a simulation language in which one can build a process-oriented model, an event-oriented model, or a combination of the two. The latest version is SLAM II. Visual SLAM is used in AweSim.
SIMAN (Simulation Analysis) was developed by Dennis Pegden in 1982 and was distributed by Systems Modeling Corporation. It is a simulation language in which one can build a process-oriented model, an event-oriented model, or a combination of the two. It is used in Arena.
1970
1980
1990
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© Copyright 2015 Dr. Jeffrey Strickland

27. Sim Language Comparisons
GPSS/H
SIMULATE
GENERATE RVEXPO(1,1.0)
QUEUE SERVER
SEIZE SERVER
LVEQ DEPART SERVERQ
TEST L N$LVEQ, 1000, STOP
ADVANCE RVEXPO(2,0.5)
STOP RELEASE SERVER
TERMINATE 1
START 1000
END
SLAM II
GEN, 1,,,,,,72;
LIM,1,1,100;
NETWORK;
RESOURCE/SERVER(1),1
CREATE,EXPON(1.0,1),1,1;
AWAIT(1),SERVER;
COLCT,INT(1),DELAY IN QUEUE,,2;
ACTIVITY,EXPON(0.5,2),,DONE;
ACTIVITY,,,CNTTR;
DONE FREE,SERVER
TERM;
CNTR TERM,1000;
END; ;
INIT;
FIN;
SIMAN
BEGIN
CREATE,,EX(1,1):EX(1,1);
MARK(1);
QUEUE, 1;
SEIZE :SERVER;
TALLY :1, INT(1);
COUNT :1,1;
DELAY :EX(2,2);
RELEASE :SERVER;
DISPOSE;
END;
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© Copyright 2015 Dr. Jeffrey Strickland

28. Visual Interactive Simulation (1)
SimProcess (SimScript II.5)
Arena (SIMAN)
The basic features of a Visual Interactive Simulation (VIS) are:
Build and modify simulation models on the screen
Execute graphical simulation models
Animate models as the execute
Present simulation output graphically
Interactively interrupt the model when it is executing to change model parameters, or operation logic and then continue execution with these modifications
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29. Visual Interactive Simulation (2)
OPNET
Extend
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30. Alan Pritsker’s Seven Principles
Conceptualizing a model requires system knowledge, engineering judgment, and model-building tools.
The secret to being a good modeler is the ability to remodel
The modeling process is evolutionary because the act of modeling reveals important information piecemeal.
The problem or problem statement is the primary controlling element in model-base problem solving.
In modeling combined systems, the continuous aspects of the problem should be considered first. The discrete aspects of the problem should then be developed.
A model should be evaluated according to its usefulness. From an absolute perspective, a model is neither good or bad, nor is it neutral.
The purpose of simulation modeling is knowledge and understanding, not models.
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© Copyright 2015 Dr. Jeffrey Strickland

31. “All models are wrong; but some are useful.” George E.P. Box
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32. © Copyright 2015 Dr. Jeffrey Strickland
References
Banks, J., Carson, J.S. II, Nelson, B., & Nicol, D.M. (2001). Discrete-Event System Simulation. Prentice Hall.
Cloud, D.J. & Rainey, L.B. (Eds.). (1998). Applied Modeling and Simulation: An integrated Approach to Development and Operation. McGraw-Hill.
Law, A.M. & Kelton, D.W. (1998). Simulation Modeling & Analysis, 2nd Ed., McGraw-Hill.
Schriber, T.J. & Brunner, D.T. (1998). How discrete-event simulation software works. In Handbook of Simulation, Wiley. Chapter 24.
Zeigler, B.P, Praehofer, H., & Kim, T.G. (2000). Theory of Modeling and Simulation, 2nd Ed., Academic Press.
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