Simulation Intro

1. Simulation And Modeling
Lec:01

2. Introduction
ByYousraZafar 2
• Instructor: EngrSyedaHinaGillani
• Email:syeda.Hina.gillani@hitecuni.edu.pk

3. Guidelines
3
• Class discipline:
• Feedback is welcome.
• Do respect each others.
• Maintain proper discipline during the online class.
• Be active.
• Quiz
• Announced
• Un-announced
• Most probably quiz will be after completion of each topic
• Assignments
• All assignments will be graded

4. Course Learning Outcome (CLO)
4
CourseLearningOutcome
Domain
PLO
By the end of this course students will able to:
1. Explain the model classificationat different level. C2
2. Analyse complex engineeringsystems and associated issues (using
systems thinking andmodellingtechniques).
C4
3. Apply advanced theory-based understanding of engineering
fundamentals and specialist bodies of knowledge in the selected
discipline areato predict the effect ofengineering activities.
C3
4. Analyse the simulation results of a medium sized engineering
problem.
C4
*BT Level=Bloom’s TaxonomyLevel
C(Cognitive Domain): C1(Remembering), C2(Understanding), C3(Applying), C4(Analyzing), C5(Evaluation),
C6(Creating)
*PLO Emphasis Level
1=low, 2=medium, 3=high

5. Evaluation criteria for assessment items
5
Assessment Items %age
Assignments 10
Quizzes 15
Term Project/ Presentation/ Demonstration/Viva 10
Class Participation 5
Mid Exam (after 8 weeks) 20
Final examination (after 16 weeks) 40
Total 100

6. Recommended books
6
• Modeling and Simulation
– Bungartz, H.-J., Zimmer, S., Buchholz, M., Pflüger,
D., Springer-Verlag, 2014.
• Simulation Modeling Handbook, A Practical
Approach
– Christopher A. Chung, CRC Press, 2004.

7. Modeling and Simulation
7
Introduction to modeling and
simulation

8. Goal of today’s lecture
8
• Introduce System
• Introduce Modeling
• Introduce Simulation
• Develop an Appreciation for the Need for
Simulation

9. What is System
9
• System
– The term system is derive from the Greek word systema, which
means an organized relationship among functioning units or
components.
– System exists because it is designed to achieve one or more
objectives
– We come into daily contact with the transportation system, the
telephone system, the accounting system, the production
system, and for two decades the computer system
– There are more than a hundred definitions of the word system,
but most seem to have a common thread that suggests that a
system is an orderly grouping of interdependent components
linked together according to a plan to achieve a specific
objective
– e.g. computer system, banking system, bus ticking system, etc.

10. What is System (cont.)
10
• The study of the systems concepts, then, has
three basic implications:
1. A system must be designed to achieve a
predetermined objective
2. Interrelationships and interdependence must
exist among the components
3. The objectives of the organization as a whole
have a higher priority than the objectives of its
subsystems

11. What is System (cont.)
11
• Entity
– An object of interest in the system
– e.g. different software
• Attribute
– a property of an entity
– E.g. version of the software
• Activity
– Represents a time period of specified length.
– Collection of operations that transform the stateof an
entity
– e.g. executing the software

12. What is System (cont.)
12
• Event
– change in the system state
– E.g., shutting down the computer
• State Variables
– Define the state of the system
– Can restart simulation from state variables
– E.g., computer is in sleep mode

13. What is Modeling
13
A Representation of an object, a system, or an
idea in some form other than that of the entity
itself.
• Types of model
– Physical
• (Scale models, prototype plants,…)
– Mathematical
• (Analytical queuing models, linear
programs, simulation)

14. What is Simulation
14
• A Simulation of a system is the operation of a
model, which is a representation of that
system
• In another word simulation is an imitation of
the reality
• Simulation has long been used by the
researchers, analysts, designers and other
professionals in the physical and non-physical
experimentations and investigations

15. Why Simulation?
15
• It may be too difficult, hazardous, or expensive to
observe a real, operational system
• The operation of the model can be studied, and, from
this, properties concerning the behavior of the actual
system can be inferred
• Uses of simulations
– Analyze systems before they are built
– Reduce number of design mistakes
– Optimize design
– Analyze operational systems
– Create virtual environments for training, entertainment

16. When to use Simulation?
16
• Over the years tremendous developments have taken place in
computing capabilities and in special purpose simulation languages,
and in simulation methodologies.
• The use of simulation techniques has also become widespread.
• Following are some of the purposes for which simulation may be
used.
– Simulation is very useful for experiments with the internal interactions of
a complex system, or of a subsystem within a complex system.
– Simulation can be employed to experiment with new designs and policies,
before implementing
– Simulation can be used to verify the results obtained by analytical
methods and reinforce the analytical techniques.
– Simulation is very useful in determining the influence of changes in
variables on the input output of the system.
– Simulation helps in suggesting modifications in the system under
investigation for its optimal performance.

17. Types of Simulation Models
17

18. Types of Simulation Models (cont.)
18
• Simulation models can be classified as being deterministic or
stochastic,static or dynamic, and discrete or continuous.
– Deterministic model
• Deterministic models have a known set of inputs, which result into unique set of
outputs. e.g. missile launch system simulation
– Stochastic model
• In stochastic model, there are one or more random input variables, which lead to
random outputs. e.g. waiting time of bank queuing system.
– Static simulationmodel
• A static simulation model represents a system, which does not change with time or
represents the system at a particular point in time e.g. simulation model of a
building
– Dynamic simulationmodel
• Dynamic simulation models represent systems as they change over time. e.g. a
simulation of a CPU

19. Types of Simulation Models (cont.)
19
– Continuous simulation model
• System in which the state of the system changes continuouslywith time
are called continuous systems. e.g. number of jobs for CPU scheduling
– Discrete simulation model
• The systems in which the state changes abruptlyat discrete points in time
called discrete systems. e.g. strength of student each day at school

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24. Steps In Simulation and Model Building
24
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

25. Steps In Simulation and Model Building
(cont.)
25
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

26. Steps In Simulation and Model Building
(cont.)
26

27. Define An Achievable Goal
27
• “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

28. Put together a complete mix
of skills on the team
28
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)

29. Put together a complete mix of
skills on the team (cont.)
29
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)

30. Involve end user
30
• 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!

31. Choose The Appropriate Simulation Tools
31
Assuming Simulation is the appropriate means,
three alternatives exist:
1. Build Model in a General Purpose
Language
2. Build Model in a General Simulation
Language
3. Use a Special Purpose Simulation Package

32. Modeling with general purpose languages
32
1. C, C++, Java
– Object-oriented programming language
• Advantages
– Little or no additional software cost
– 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

33. Modeling with general simulation languages
33
1.
2.
GPSS (General PurposeSimulationSystem)
– Block-structured Language
– Interpretive Execution
– FORTRAN-based (Help blocks)
– World-view: Transactions/Facilities
MODSIM III
– Modern Object-Oriented Language
– Modularity Compiled Programs
– Based on Modula2 (but compiles into C)
– World-view: Processes
• 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

34. Modeling with special purpose simulation
languages
34
1. OPNET
– Simulator for communication networks, including wireless networks
2. SIMULINK
– Simulink is a simulation and model-based design environment for
dynamic and embedded systems,integrated with MATLAB
• Advantages
– Very quick development of complex models
– Short learning cycle
– No programming--minimalerrors in usage
• Disadvantages
– High cost of software
– Limited scope of applicability
– Limited flexibility (may not fit your specific application)

35. Model the appropriate level(s) of detail
35
• Define the boundaries of the system to be
modeled.
• Some characteristics of “the environment”
(outside the boundaries) may need to be
included in the model.
• Not all subsystems will require the same level of
detail.
• Control the tendency to model in great detail
those elements of the system which are well
understood, while skimming over other, less well-
understood sections.

36. Start early to collect the necessary input data
36
• Data comes in two quantities
– TOO MUCH!!
• With too much data, we need techniques for
reducing it to a form usable in our model.
– TOO LITTLE!!
• With too little data, we need information
which can be represented by statistical
distributions

37. Provide adequate and on-going
documentation
37
• In general, programmers hate to document.
(They love to program!)
• What can we do?
– Insist on built-in user instructions(help screens)
– Set (or insist on) standards for coding style

38. Develop plan for adequate model verification
38
• Did we get the “right answers?”
• 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

39. Develop a plan for model validation
39
• 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?

40. Advantages of Simulation
40
• Simulation helps to learn about real system, without
having the system at all. For example the wind tunnel
testing of the model of an aero plane does not require
a full sized plane
• Many managerial decision making problems are too
complex to be solved by mathematical programming
• In many situations experimenting with actual system
may not be possible at all. For example, it is not
possible to conduct experiment, to study the behavior
of a man on the surface of moon.
• In some other situations, even if experimentation is
possible, it may be too costly and risky.

41. Advantages of Simulation (cont.)
41
• In the real system, the changes we want to study
may take place too slowly or too fast to be
observed conveniently.
• Computer simulation can compress the
performance of a system over years into a few
minutes of computer running time
• Conversely, in systems like nuclear reactors where
millions of events take place per second,
simulation can expand the time to required level

42. Advantages of Simulation (cont.)
42
• Through simulation, management can foresee the
difficulties and bottlenecks, which may come up due to
the introduction of new machines, equipment's and
processes. It thus eliminates the need of costly trial
and error method of trying out the new concepts
• Simulation being relatively free from mathematics can
easily be understood by the operating personnel and
non-technical managers. This helps in getting the
proposed plans accepted and implemented.
• Simulation Models are comparatively flexible and can
be modified to accommodate the changing
environment to the real situation.

43. Advantages of Simulation (cont.)
43
• Extensive computer software packages are available,
making it very convenient to use fairly sophisticated
simulation models.
• Simulation is a very good tool of training and has
advantageously been used for training the operating and
managerial staff in the operation of complex system.
• Space engineers simulate space flights in laboratories to
train the future astronauts for working in weightless
environment.
• Airline pilots are given extensive training on flight
simulators, before they are allowed to handle real planes.

44. Disadvantages of Simulation
44
• Model building requires special training. It is an art that is
learned over time and through experience. Furthermore, if
two models are constructed by two competent individuals,
they may have similarities, but it is highly unlike that they
will be the same.
• Simulation results may be difficult to interpret. Since most
simulation outputs are essentially random variables, it may
be hard to determine whether an observation is a result of
system interrelations or randomness.
• Simulation is used in some cases when an analytical
solution is possible, or even preferable.
• Simulation modeling and analysis can be time consuming
and expensive.

45. Applications of Modeling and Simulation
45
• Manufacturing
– Design analysis and optimization of production
system, materials management, capacity planning,
layout planning, and performance evaluation,
evaluation of process quality.
• Business
– Market analysis, prediction of consumer behavior,
and optimization of marketing strategy and
logistics, comparative evaluation of marketing
campaigns.

46. Applications of Modeling and Simulation
(cont.)
46
• Military
– Testing of alternative combat strategies, air
operations, sea operations, simulated war
exercises, practicing ordinance effectiveness,
inventory management.
• Healthcare applications
– Planning of health services, expected patient
density, facilities requirement, hospital staffing ,
estimating the effectiveness of a health care
program.

47. Applications of Modeling and Simulation
(cont.)
• Communication Applications
– Such as network design, and optimization, evaluating
network reliability, manpower planning, sizing of
message buffers.
• Computer Applications
– Such as designing hardware configurations and operating
system protocols, sharing networking. Client/Server
system architecture
• Economic applications
– Such as portfolio management, forecastingimpact of
Govt. Policies and market on the international
fluctuations economy. Budgeting and forecastingmarket
fluctuations.
47

48. Applications of Modeling and Simulation
(cont.)
48
• Transportation applications
– Design and testing of alternative transportation policies,
transportation networks-roads, railways, airways etc. Evaluation
of timetables, traffic planning.
• Environnent application
– Solid waste management, performance evaluation of
environmental programs, evaluation of pollution control
systems.
• Biological applications
– Such as population genetics and spread of epidemics.
• Business process Re-engineering
– Integrating business process re-engineering with image –based
work flow, using process modeling and analysis tool.

49. Applications of Modeling and Simulation
(cont.)
49

50. Summary
50
• Modeling and simulation is an important, widely
used technique with a wide range of applications
– Computation power increases (Moore’s law) have
made it more pervasive
– In some cases, it has become essential (e.g., to be
economically competitive)
– Rich variety of types of models, applications, uses
• Appropriate methodologies required to protect
against major mistakes.