SIMULATION AN INTRODUCTION "The technique of imitating the behavior of some situation or system...

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.

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.

ADVANTAGES AND DISADVANTAGES OF SIMULATIONAdvantages

1. 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.

2. 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.

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.

4. 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.

ADVANTAGES AND DISADVANTAGES OF SIMULATIONAdvantages continue…

5. 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.


7. 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

8. Build consensus

Using simulation to present design changes creates and objective opinion

avoiding inferences when you approved or disapproved a design.


9. Prepare for change

Help to answer all the what-if questions which are useful for both designing new

systems and redesigning existing systems.

10. Invest wisely

Cost of simulation is less than 1% of total amount being expanded for

implementation of a design or redesigning.


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 learnby 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.


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.


ADVANTAGES AND DISADVANTAGES OF SIMULATIONDisadvantages

1. 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.

2. 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.

ADVANTAGES AND DISADVANTAGES OF SIMULATIONDisadvantages

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.

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 Resourcese.g. Evaluation on water quality data


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


Computer simulation

Computational science

Complex System Virtual reality

Artificial life Physically based modeling

SupercomputersImmersiveHardwareinterface

Computer graphicand animation

Theoreticalbiology

Massive parallelism

Figure 1.0 Role of Computer Simulation

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

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



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

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



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

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

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).

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.

INPUT DATA

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

SIMULATION MODELING

System model

Deterministic Stochastic

Static Dynamic Static Dynamic

Continuous Discrete Continuous Discrete

Discrete-event simulation

Monte Carlo simulation

Figure 1.2 System Model Taxonomy

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

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


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

1. Defining changes in state that occur at each event time

2. Describing the process (network) through which the entities in the model flow

3. Describing the activities in which entities engage

4. Describing the objects (entities) and the condition that change the state of the

objects

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.


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.





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

TO BE CONTINUED …

SIMULATION AN INTRODUCTION "The technique of imitating the behavior of some situation or system...

Documents

Transcript of SIMULATION AN INTRODUCTION "The technique of imitating the behavior of some situation or system...