1. "The technique of imitating the behavior of some situation or
SIMULATION AN INTRODUCTION
"The technique of imitating the behavior of some situation or
system (economic, mechanical, etc.) by means of an analogous model,
situation, or apparatus, either to gain information more conveniently
or to train personnel.“
Oxford English Dictionary
simulation is the technique of a building a model of a real or proposed system so that the behavior of the system under specific conditions may be studied.

2. One of the key powers of simulation is the ability to model
the behavior of a system as time progresses.
Simulation attempts to build an experimental device that will
act like a real system in important aspects.
It is important to note that simulation models are descriptive, not prescriptive.
They tell how a system works under given conditions; not how to arrange
the conditions to make the system work best.

3. ADVANTAGES AND DISADVANTAGES OF SIMULATION
Choose correctly
Simulation lets user test every aspect of a proposed change or addition without
committing resources to their acquisition. This is critical, because once the
final decision have been made, material and systems have been installed,
changes and correction can be extremely expensive.
Compress and expand time
By compressing or expanding time, simulation allows you to speed up or slow
down phenomena so that you can investigate them thoroughly. You can examine
an entire shift in matter of minutes if desire, or you can spend 2 hours examining
all the events that occurred during 1 minute of simulated activity.

4. ADVANTAGES AND DISADVANTAGES OF SIMULATION
Advantages continue…
3. Explore possibilities
Once a valid simulation model have been developed, user can explore new
policies, operating procedures, or method without the expense and disruption
of experimenting with real system. Modifications are incorporated in the model,
and you observe the effects of those changes on computer rather than on real
life system.
Diagnose Problem
Simulation allows you to better understand the interactions among variables that
make up certain complex systems. Diagnosing problems and insight into the
importance of these variables increases user understanding of their important
effects on the performance of all overall system.

5. ADVANTAGES AND DISADVANTAGES OF SIMULATION
Advantages continue…
Identify constraints
Simulation can be used to discover the cause of delays in work in process
information, materials, or other processes.
6. Develop understanding
Simulation studies aid in providing understanding about how a system really
operates rather indicating someone’s predictions only about how a system
will operate.

6. ADVANTAGES AND DISADVANTAGES OF SIMULATION
Advantages continue…
Visualize the plan
Depending on software used, you maybe able to view operations from various
angles and level of magnification, even in 3D. This allows to detect flaws that
appear credible when seen just on paper 2D drawing
Build consensus
Using simulation to present design changes creates and objective opinion
avoiding inferences when you approved or disapproved a design.

7. ADVANTAGES AND DISADVANTAGES OF SIMULATION
Advantages continue…
9. Prepare for change
Help to answer all the what-if questions which are useful for both designing new
systems and redesigning existing systems.
Invest wisely
Cost of simulation is less than 1% of total amount being expanded for
implementation of a design or redesigning.

8. ADVANTAGES AND DISADVANTAGES OF SIMULATION
Advantages continue…
11. Train the team
Simulation models can provide excellent training when designed for that
purpose. The team only needs to provides decision input to the simulation
model as it progresses. Team and individual members of team, can learn
by their mistakes and learn to operate better. It is less expensive and less
disruptive than the on-the-job learning.
12. Specify requirements
Simulation can be used to specify requirements for a system design. For
example, the specifications for a particular type of machine in a complex
system to achieve a desired goal maybe unknown. By simulating different
capabilities for the machine, the requirements can be establish.

9. ADVANTAGES AND DISADVANTAGES OF SIMULATION
Advantages continue…
13. Understand why
Managers often want to know why certain phenomena occur in real
system. With simulation, you determined the answer to the “why”
questions by reconstructing the scene and taking microscopic examination
of the system to determined why the phenomenon occurs. You cannot
accomplish this with a real system because you cannot see or control it in
its entirety.

10. ADVANTAGES AND DISADVANTAGES OF SIMULATION
Model Building Requires Special training
It is difficult to build the best model. If two models of the same system are
constructed by two competent individuals, they may have similarities
but is highly unlikely that they will be the same.
Simulation modeling and analysis can be time consuming and expensive
Skimping on resources for modeling and analysis may result in a simulation
model and/or analysis that is not sufficient to the task.

11. ADVANTAGES AND DISADVANTAGES OF SIMULATION
3. Simulation result may be difficult to interpret
Difficult to determined whether an observation is a result of system
interrelationship or randomness as the inputs are random.
4. Simulation may be used inappropriately
Simulation is used in some cases when an analytical solution is possible,
or even preferable. This is particularly true in the simulation of some waiting
lines where closed-form queuing models are available, at least for long-run
evaluation.

12. AREAS OF APPLICATION
1. Manufacturing and Material Handling Application
e.g. Minimizing delays of prefabricated parts before assembly
Material flow analysis of automotive assembly plants
2. Public System Application
e.g. Timing of liver transplant
Evaluation of nurse-staffing and patient population scenarios
3. Military System
e.g. Evaluation of theater airlift system productivity
4. Natural Resources
e.g. Evaluation on water quality data

13. AREAS OF APPLICATION
5. Public Services
e.g. Emergency ambulance system analysis
6. Service System Applications
6.1 Transportation
e.g. Animation of a toll plaza
6.2 Computer Systems Performance
e.g. Evaluation of database transaction management protocols
6.4 Communication Systems
e.g. Evaluation modeling of broadband telecommunication networks

14. AREAS OF APPLICATION
Computational
science
Complex System
Virtual reality
Supercomputers
Immersive
Hardware
interface
Massive
parallelism
Computer simulation
Computer graphic
and animation
Theoretical
biology
Artificial life
Physically based modeling
Figure 1.0 Role of Computer Simulation

15. PRINCIPLES OF SIMULATION
Problem formulation
Every simulation begins with problem statement
If statement from client then make sure problem is clearly understood
If statement by analyst make sure client agree and understand formulation
Set of assumptions are being prepare and agreed
Setting Objectives and overall project plan
Preparing proposal
Objectives are question to be answered by simulation study
Project plan include various scenarios to be investigate
Planning time (Gantt chart), hardware/software, and personnel to be used

16. PRINCIPLES OF SIMULATION
Model Conceptualization
A conceptual model to abstract real world system
Client should be involved in model construction
Begin with a simple model and expand it by adding elements phase by phase
Data collection
Schedule of data requirements submitted to the client
Data needed should have been collected by client and submitted via electronic format. Model building and data collecting can be contemporaneous

17. PRINCIPLES OF SIMULATION
Model translation
Conceptual model is coded into computer-recognizable form/operational model
Verified?
Verification of the operational model performance
Must be a continuing process
Entire model must be complete before performing verification
Use interactive controller or debugger as an aid

18. PRINCIPLES OF SIMULATION
Validated?
Validation is the determination that the conceptual model is an accurate representation of real system. If there is an existing system, validate by comparing its output to of a base system
Experimental Design
For each scenarios that is to be simulated decisions need to be made to determine :
length of the simulation run
number of run (replications)
manner of initialization

19. PRINCIPLES OF SIMULATION
Production runs and analysis
Used to estimate measures of performance for the scenarios that are being simulated
More runs?
Based on analysis of runs that have been completed
Determines if additional runs needed or other scenarios to be simulated

20. PRINCIPLES OF SIMULATION : See Figure 1.1
Documentation and reporting
Result of all the analysis should be reported clearly and concisely
Enable user to review
final formulation
alternatives that were addressed
criterion by which the alternative systems were compared
result of the experiments
analyst recommendations
Implementation
Simulation need to lead to some concrete action by customer.
If system improves as a result of this action then the project is consider successful

21. INPUT DATA
The role of random numbers
One aspect of simulation that is often confusing is the role of random numbers.
How can the computer generate randomness? And how can ``random''
simulations be repeated over and over again. In some sense, the confusion is
justified, for a computer cannot generate true randomness. It can only generate
pseudo--randomness.
Pseudo--random numbers can be generated many ways. The most common
is by a linear congruential method (LCM), a complicated word for a simple concept.
Let's suppose I want to generate random numbers between 0 and 15 (integers only).

22. INPUT DATA
We will need to begin with a single number, perhaps created by rolling a 16
sided dice. Suppose that number is 7. A LCM with parameters (5,3,16) would
multiply that 7 by 5, add 3, and divide by 16 taking the remainder:
Our second ``random number'' is 6. We can repeat this process:
and so on. This sequence is not random; there is a formulae that generates
any number based on any previous number. However, it does pass many
statistical tests for randomness and hence can be used like a random sequence.

23. INPUT DATA
Techniques of handling random data may varies according to
The amount of available data
whether data collected and confirmed through test or just best guess
whether variable is independent or related to other inputs

24. Figure 1.2 System Model Taxonomy
SIMULATION MODELING
System model
Deterministic
Stochastic
Static
Dynamic
Continuous
Discrete
Discrete-event simulation
Monte Carlo simulation
Figure 1.2 System Model Taxonomy

25. SIMULATION MODELING
A system model is deterministic or stochastic. A deterministic system has no
stochastic (random) components.
A system model is static or dynamic. A static system model is one in which time
is not a significant variable.
A dynamic system model is continuous or discrete. Discrete system have variables
that changes values on at discrete times when job arrives or depart.
A discrete-event simulation model is defined by 3 attributes :-
Stochastic – at least some of the system-state variables are random
Dynamic – the time evolution of the system-state variables is important
Discrete – significant changes in the system-state variables are associated with
events that occur at discrete time instances only

26. DISCRETE EVENT MODEL
The components that flow in discrete system like people, equipments, orders
And raw materials are called entities.
There are many types of entities and each has set of characteristic or attributes
Grouping of entities are called files, set, list or chains.
Goal of model is to portray activities in which entities engaged and learn the
system dynamic behavior
This is accomplish by defining states of a system

27. DISCRETE EVENT MODEL
The beginning and ending of each activities are called events
The state of model remain constant between consecutive event times, and a complete
dynamic portray of the state of the model is obtained by advancing simulated time
from one event to the next.
This is referred to as next-event approach and is used in many discrete simulation
The formulae is as follows
Defining changes in state that occur at each event time
Describing the process (network) through which the entities in the model flow
Describing the activities in which entities engage
Describing the objects (entities) and the condition that change the state of the
objects

28. DISCRETE EVENT MODEL
Example : Discrete Event Model of the Banking system
A banking system will consist of different events
This simple example will illustrate the basic concepts of discrete-event modeling
As we know variables, entities, and files membership make up the static structure
of a simulation model.
They describe the state but not how it operates. The events then will specify the
logic that controls changes at a specific time.

29. DISCRETE EVENT MODEL
Example : Discrete Event Model of the Banking system
For instance a bank consist of 2 major events Customer arrival event and End-service event.
The logic flow of a banking process is that a customer comes into the bank. Take a number ( wait in a queue) if the teller is fully occupied, the bank teller assist the customer in her/his banking operation, the customer leave the bank next customer will be attended.
The flow is shown by the logic diagrams.

30. DISCRETE EVENT MODEL
Example : Discrete Event Model of the Banking system
Diagram of a banking system : See Figure 1.3
Customer-arrival event Logic : See Figure 1.4
End-of-service event Logic : See Figure 1.5
Exercise
In groups of two discuss on the possible events in a car wash system.
Draw it’s model diagram and draw it’s events Logic

31. TO BE CONTINUED …