w1 - introduction to simulation (3)

SyllabusWeek

01TI141317 – Simulasi Sistem Industri

Lab. KOI Jurusan Teknik Industri - ITS

Industrial System Simulation

• Instructor:

Nurhadi Siswanto, PhD

Stefanus Eko Wiratno, MT

Effi Latiffianti, M.Sc.

Diesta Iva Maftuhah, MT

• References:

Kelton, David W, Randall P Sadoswski, and David T Sturrock,Simulation with Arena, Mc Graw Hill.

Harrel, Ghosh, Bowden (2004), Simulation using Promodel,McGraw Hill.

Birta, Louis G, Arbez (2007), Modelling and Simulation, Springer – VerlagLondon.

Seldon, M Ross (2006), Simulation, Elsevier.


Course Description

• Simulation is defined as a technique to imitate process/operation

by using computer from a complex system which is difficult to

model (cannot be modelled) as mathematical formulation.

• Simulation model is designed to be used for studying system by

conducting experiments to achieve the desired

objectives/performance measurements.

• This course deals on how to design the right simulation model. So

that when the students have finished studied, they are able to

develop valid simulation models and conduct experiments by using

these models.

• The learning activities consist of lecturing which discusses

simulation concept and simulation modelling techniques as well as

assigning a real case study which will be presented at the end of

the period.


Learning Outcome

Upon completion of the subject, students will be

able to develop valid simulation models and

conduct experiments by using these models.


Grading System

Grading consists of:

• Final Project & Assignment (15%)

• Quiz 1 (15%)

• Quiz 2 (15%)

• Midterm Exam (25%)

• Final Exam (30%)


Final Project

• Deadline is strictly followed. If it is overdue,

you do not get grade.

• Choose a real case to simulate by using

software ARENA 14.

• You are assigned to make a group consists of 4

people (max.)


Course Schedule

Week Topic

1 Introduction to Simulation

2 System Dynamics

3System Dynamics – Case Study

Simulation Basic

4 Discrete Event Simulation

5 Software Tutorial: ARENA

6 Model Building

7 Midterm Exam

8 Midterm Exam


Course Schedule

Week Topic

9 Data Collection & Input Analysis

10 Output Analysis

11 Comparing System and Scenarios

12 Presentation – Final Project (Progress Report)

13 Advanced module in ARENA

14 Presentation – Final Project

15 Final Exam

16 Final Exam

Introduction

to SimulationWeek

01TI141317 – Simulasi Sistem Industri


What is Simulation?

• The Oxford American Dictionary (1980) defines

Simulation as a way “to reproduce the conditions of

a situation, as by means a model, for study or

testing or training, etc.”

• We are interested in reproducing the operational

behavior of dynamic systems

• The model that we will be using is a computer model


What is Simulation?

• Simulation is the imitation of a dynamic system using a computer model in order to evaluate and improve system performance (Schriber, 1987)

• In practice, simulation is usually performed using commercial simulation software –e.g. Arena, Awesim, ProModel, AutoMod,, etc.


What Are We Interested In?

Reproducing the operational

behaviour of dynamic system

TOOL Computer

It’s usually performed using commercial simulation software

such as: Arena, Awesim, Promodel, etc

Simulation Examples?


Why is simulation used?

• Is there any other way but simulation?

Playing with the system

traditional trial-and-error techniques

No longer adequate

And Not always work

Simulation



The nature of operations system

– VariabilityVariations are predictable or unpredictable

– InterconnectednessComponents of the system do not work in isolation, but affect

one another

– ComplexityComplexity = variability + interconnectedness



• Simulation provides a way to validate whether or

not the best decisions are being made.

• Simulation avoids the expensive, time consuming,

and disruptive nature of traditional trial-and-error

techniques.

• The power of simulation: it provides a method of

analysis that is not only formal and predictive, but

is capable of accurately predicting the performance

of even the most complex dynamic systems.

• Management of a system in action based on

prediction [Deming, 1989]


Why is simulation used ?

• The characteristics of simulation that make it

such a powerful planning and decision making

tool:

– Captures system interdependencies

– Accounts for variability in the system

– Is versatile enough to model any system

– Show behavior over time

– Is less costly, time consuming, and disruptive than experimenting on the actual system


Characteristics of Simulation

– Provides information on multiple performance

measures

– Is visually appealing and engages people’s interest

– Provides results that are easy to understand and

communicate

– Runs in compressed, real, or even delayed time

– Forces attention to detail in a design


Doing Simulation

• Simulation is essentially an experimentation tool in which a computer model of a new or existing system is created for the purpose of conducting experiment

• Doing simulation: “the process of designing a model of a real system and conducting experiment with this model” (Shannon, 1998).

• Conducting experiments on a model reduces the time,

cost, and disruption of experimenting on the actual

system.

– Simulation is a virtual prototyping tool for

demonstrating proof of concept.


Doing Simulation

• Simulation provides a virtual method for doing system

experimentation


Procedure for Doing Simulation

1. Formulating a hypothesis

2. Setting up an experiment

3. Testing the hypothesis

through experiment

4. Drawing conclusions about

the validity of the

hypothesis

Start

End

Formulate a hypothesis

Develop a

Simulation model

Run simulation experiment

Hypothesis

correct?

Yes

No


Use of Simulation

• Initial models were programmed in FORTRAN –

often consisted of thousands of lines of code

• Took a year or more to build and debug

• Ran in batch mode on expensive mainframe

computers

• Used a commercial software since 1960

• Gained popularity as a decision-making tool in

manufacturing and service industries in the

last two decades


Use of Simulation

Popularity of computer simulation can be attributed to the following:

Increased awareness and understanding of simulation technology

Increased availability, capability, and ease of use of simulation software

Increased computer memory and processing speeds, especially of PC’s

Declining computer hardware and software costs


Use of Simulation

• Simulation is no longer considered a method of

“last resort”, nor is it a technique reserved only

for simulation “experts” (easy-to-use simulation

software).

• The use of simulation in designing and improving

business processes of every kind will likely to

grow

• An increasing use of simulation is in the area of

communication and visualization

• Computer based training


Use of Simulation

Typical Applications:– Capacity planning

– Cycle time reduction

– Staff and resource planning

– Bottleneck analysis

– Quality improvement

– Cost reduction

– Productivity improvement

– Layout analysis

– Investment analysis

– Etc.


When Simulation is Appropriate

• Not all system problems could be solved with the aid of simulation

• It is important to select the right tool for the task

• Can be used to study simple models but should not use it if an analytical solution is available

• Real power of simulation is in studying complex models

• Simulation can tolerate complex models since we don’t even aspire to an analytical solution


When Simulation is Appropriate

• Simulation is appropriate if the following criteria hold true:

– An operational (logical or quantitative) decision is being made

– The process being analyzed is well defined and repetitive

– The activities and events are interdependent and variable

– The cost impact of the decision is greater the cost of doing the simulation

– The cost to experiment on the actual system is greater than the cost of simulation


Qualification for Doing Simulation

• Some training is required to use simulation

• But it does not mean that only statisticians and operations research specialists can learn how to use it

• Decision support tools are always more effective when they involve the decision makers

• To make simulation more accessible to non-simulation experts, modelers should be aware of their own inability in dealing with the statistical issues associated with simulation.


Qualification for Doing Simulation

To gain the greatest benefits from simulation, a certain knowledge and skill in the following areas is useful:

– Project management

– Communication

– System engineering

– Statistical analysis and design of experiments

– Modeling principles and concepts

– Basic programming and computer skills

– Training on one or more simulation products

– Familiarity with the system being investigated


Economic Justification of Simulation

Cost is always important issues when considering the use of any software tool and simulation is no exception.

Simulation should not be used if the cost exceeds the expected benefits.

This means that both the costs and the benefits should be carefully assessed.

The use of simulation is often prematurely dismissed due to the failure to recognize the potential benefits and savings it can produce.

Savings from simulation are realized by identifying and

eliminating problems and inefficiencies.

Cost is reduced by eliminating overdesign and removing

excessive safety factors.



One of the difficulties in developing an economic

justification for simulation is the fact that it is usually

not known in advance how much savings will be

realized.

One way to assess in advance the economic benefit of

simulation is to assess the risk of making poor design

and operational decisions.

The real savings from a simulation come from allowing

to make mistake and work out design errors on the

model rather than on the actual system.

Simulation helps avoid many of the downstream costs

associated with poor decision that are made up front.



Simulation should not be used if the cost exceeds the

expected benefits.

Cost

System stage

Concept Design Installation Operation



Syste

m c

osts

Design

phase

Implementation

phase

Operation

phase

Cost without

simulation

Cost with

simulation

Comparison of cumulative system costs

with and without simulation


Advantages of Simulation

• Flexibility to model things as they are (even if messy

and complicated)

• Allows uncertainty, nonstationarity in modeling

• Advances in computing/cost ratios

• Advances in simulation software


Disadvantages of Simulation

• Don’t get exact answers, only approximations,

estimates

• Get random output (RIRO) from stochastic

simulations


When Simulations are Used ?

• Uses of simulation have evolved with hardware,

software

• The early years (1950s-1960s)

– Very expensive, specialized tool to use

– Required big computers, special training

– Mostly in FORTRAN (or even Assembler)

– Processing cost as high as $1000/hour for a sub-286

level machine



• The formative years (1970s-early 1980s)

– Computers got faster, cheaper

– Value of simulation more widely recognized

– Simulation software improved, but they were still

languages to be learned, typed, batch processed

– Often used to clean up “disasters” in auto,

aerospace industries

• Car plant; heavy demand for certain model

• Line underperforming

• Simulated, problem identified

• But demand had dried up — simulation was too late



• The recent past (late 1980s-1990s)

– Microcomputer power

– Software expanded into GUIs, animation

– Wider acceptance across more areas

• Traditional manufacturing applications

• Services

• Health care

• “Business processes”

– Still mostly in large firms

– Often a simulation is part of the “specs”



• The present

– Proliferating into smaller firms

– Becoming a standard tool

– Being used earlier in design phase

– Real-time control

• The future

– Exploiting interoperability of operating systems

– Specialized “templates” for industries, firms

– Automated statistical design, analysis


Using Computers to Simulate

• General-purpose languages (FORTRAN)

– Tedious, low-level, error-prone

– But, almost complete flexibility

• Support packages

– Subroutines for list processing, bookkeeping, time

advance

– Widely distributed, widely modified

• Spreadsheets

– Usually static models

– Financial scenarios, distribution sampling, SQC


Using Computers to Simulate

• Simulation languages

– GPSS, SIMSCRIPT, SLAM, SIMAN

– Popular, still in use

– Learning curve for features, effective use, syntax

• High-level simulators

– Very easy, graphical interface

– Domain-restricted (manufacturing, communications)

– Limited flexibility — model validity?

w1 - introduction to simulation (3)

Documents

simulation concept

right simulation model

valid simulation models

commercial simulation

complex system

final project assignment

system performance schriber

software arena