02 Introduction to Simulation

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Lecture 1

INTRODUCTION TOSIMULATION

Lecture 1

INTRODUCTION TOSIMULATION

Simulasi [email protected] 1

Winda Nur Cahyo, ST., MT.Faculty of Industrial TechnologyIslamic University of [email protected]

[email protected](YM, FB)Website: www.teknik-industri.com

What is Simulation?• The Oxford American Dictionary (1980):

– Simulation is a way “ to reproduce the conditions of asituation, as by means of a model, for study ortesting or training, etc. ”

• For our ur oses we are interested in


reproducing the operational behavior of dynamicsystems.

• The model that will be using is a computermodel.

• Simulation can be defined as the imitation of adynamic system using a computer model.

What simulation …

• Schriber (1987) – Simulation is “ the modeling of a process or

system in such a way that the model mimicsthe response of the actual system to events


that take place over time. ”

• By studying the behavior of the model,insight about the behavior of the actualsystem can be gained.

• In practice, – Simulation is performed using commercial

simulation software. – Performance statistics are gathered during

the simulation – Modern simulation software provides a

realistic, graphical animation of the system


being modeled. – During the simulation, the user can

interactively adjust the animation speed andchange model parameter values to do “ what-if” analysis on the fly.

– State-of-the art simulation technologyprovides optimization capability

• This lecture focuses primarily on discrete-event simulation, which models the effects ofthe events in a system as they occur overtime.

• Discrete-event simulation employs statisticalmethods for generating random behavior and


es ma ng mo e per ormance.• These methods are sometimes referred to as

Monte Carlo methods because of theirsimilarity to the probabilistic outcomes foundin games of chances.

Why Simulate?• Simulation provides a way to validate whether or

not the best decisions are being made.• Simulation avoid the expensive, time-

consuming, and disrupted nature of traditionaltrial-and-error techniques.

• The power of simulation lies in the fact that it


prov es a me o o ana ys s a s no on yformal and predictive, but is capable ofaccurately predicting the performance of asystem.

• By using a computer to model a system before itis built or to test operating policies before theyare actually implemented, many of the pitfallscan be avoided



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• The characteristics of simulation that make it such apowerful planning and decision-making tool: – captures system interdependence – accounts for variability in the system – is versatile enough to model any system – shows behavior over time – is less costly, time-consuming, and disruptive that


– 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 nearly always performed as a part

of a larger process of system design or processimprovement.

• Alternative solutions are generated andevaluated, and the best solution is selected and


implemented.• Simulation comes into play during the evaluation

phase.• Simulation is an experimentation tool in which

a computer model of a new or existing system iscreated for the purpose of conductingexperiments.

Doing Simulation …

Simulation provides a virtual method for doingsystem experimentation


Doing Simulation … .

• Doing simulation talks about “ the processof designing a model of a real system andconducting experiments with this 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…

..• The procedure for doing simulation follows

the scientific method of: – formulating a hypothesis,


– , – testing the hypothesis through

experimentation – drawing conclusions about the validity of the

hypothesis.

The process ofsimulation experimentation

START FORMULATE A HYPOTHESIS

DEVELOP A SIMULATION


RESUME A SIMULATIONEXPERIMENT

HYPOTHESISCORRECT?

STOPYESYES NONO



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Use of Simulation

• Simulation began to be used incommercial applications in 1960s. – Initial models were usually programmed in


.

• Only in the last couple of decades hassimulation gained popularity as a decision-making tool in manufacturing and serviceindustries

Use of Simulation ………

• The surge in popularity of computersimulation: – Increased awareness and understanding of


. – Increased availability, capability, and ease of

use of simulation software. – Increased computer memory, processing

speeds, especially of PCs. – Declining computer hardware and software

costs.

Typical Applications ofSimulation

• Work-flow planning• Capacity planning• Cycle time reduction• Staff and resource

• Throughput analysis• Productivity improvement• Layout analysis• Line balancing


planning• Work prioritization

• Bottleneck analysis• Quality improvement• Cost reduction• Inventory reduction

• Batch size optimization• Production scheduling

• Resource scheduling• Maintenance scheduling• Control system design

When Simulation isAppropriate

• Not all system problems that could besolved with the aid of simulation should besolved using simulation,


•task.

• Simulation has certain limitations of whichone should be aware before making adecision to apply it to a given situation.

When Simulation is Appropriate… ..

• As a general guideline, simulation isappropriate if – An operational (logical or quantitative) decision is

being made.


– The process being analyzed is well defined andrepetitive.

– Activities and events are interdependent and variable. – The cost impact of the decision is greater than the

cost of doing the simulation. – The cost of experiment on the actual system is

greater than the cost of simulation.

Qualification for DoingSimulation

• Participants in the simulation project includemodeler, decision maker, and process owner.

• A certain degree of knowledge and skill: – 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



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Economic Justification ofSimulation

• Cost is always important issues whenconsidering the use of any software tool andsimulation is no exception.

• Simulation should not use if the cost exceeds


.• 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 thepotential benefits and savings it can produce.

• Savings from simulation are realized byidentifying and eliminating problems and

inefficiencies.• Cost is reduced by eliminating overdesign and

removing excessive safety factors.• One of the difficulties in develo in an economic


justification for simulation is the fact that it isusually not known in advance how much savingswill be realized.

• One way to assess in advance the economicbenefit of simulation is to assess the risk ofmaking poor design and operational decisions.

Economic Justification of Simulation……

• The real savings from a simulation comefrom allowing to make mistake and workout design errors on the model rather than


.• Simulation helps avoid many of the

downstream costs associated with poordecision that are made up front.

Cost of making changes at subsequent stagesof system development


Comparison of cumulative system costs withand without simulation


System Approach


Minggu ke 1 bagian 2



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System Definition

• A system is defined as a collection ofelements that function together to achievea desired goal.


• – A system consists of multiple elements. – These elements are interrelated and work in

cooperation. – A system exists for the purpose of achieving

specific objectives.

• Examples of systems: – Traffic systems

– Political systems – Economic systems – Manufacturing systems – Service systems


• Main focus of our subject – manufacturing and service systems that

process materials, information, and people.

Manufacturing System

• Manufacturing systems: – Small job shops – Machining cells


– arge pro uc on ac es – Assembly lines

– Warehousing – Distribution – Supply chain systems

Service System

• Service systems: – Health care facilities – Call centers


– musemen par s – Public transportation systems

– Restaurant – Bank – etc

• Both manufacturing and service systemsmay be termed processing systems . – They process items through a series of

Processing Systems


..

• Processing systems: – Artificial (human-made) – Dynamic (elements interact overtime) – Usually stochastic (they exhibit random

behavior)

System Elements• From a simulation perspective, a system

consists of entities , activities , resources ,controls .


, , ,when , and how of entity processing.



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System : Input-Output Box


• items processed through the system such as

products, customers, and documents.• divided into:

• human or animate (customers, patients, etc.)• inanimate (parts, documents, bins, etc.)

Entities


• intangible (calls, electronic mail, etc.)

• [For most manufacturing and service systems]discrete items.

• [For some production systems: continuoussystems] nondiscrete substance

• Example: oil refineries, paper mills

Activities• the tasks performed in the system (directly or

indirectly) in the processing of entities.• Servicing a customer• cutting a part on machine• repairing a piece of equipment

• consume time and often involve the use of


resources• classified as

• entity processing (check-in, treatment, inspection, fabrication,etc.)

• entity and resource movement (forklift travel, riding in anelevator, etc.)

• resource adjustments, maintenance, and repairs (machinesetups, copy machine repair, etc.)

Resources

– the means by which activities are performed. – provide the supporting facilities, equipment,

and personnel for carrying out activities. – can constrain rocessin b limitin the rate


at which processing can take place.

– have characteristics, e.g. capacity, speed,cycle time, and reliability.

Resources

– can be categorized as:• Human or animate (operators, doctors,

maintenance personnel, etc.)• Inanimate (equipment, tooling, floor space, etc.)


• Intangible (information, electrical power, etc.)

– also can be classified as• dedicated or shared• permanent or consumable• mobile or stationary

Controls – dictate how, when, and where activities are

performed. – impose order on the system. – [at the highest level] consists of schedules,

lan and olicies


, . – [at the lowest level] take the forms of written

procedures and machine control logic. – [at all levels] provide the information and

decision logic for how the system shouldoperate.



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Controls

– Examples:• Routing sequences• Production plans•


• Task prioritization• Control software• Instruction sheets

System Complexity• Elements of a system operate with one another

in ways that often result in complex interactions.• Unaided human intuition is not very good at

analyzing and understanding complex systems.• Inability of the human mind to grasp real-world

complexity is called as “ the principle of bounded”


.• This principle states that “ the capacity of human

mind for formulating and solving complexproblem is very small compared with the size ofproblem whose solution is required forobjectively rational behavior in the real world, oreven for a reasonable approximation to suchobjective rationality (Simon, 1957).

System Complexity

• is a primary function of two factors: – Interdependencies between elements so that

each element affects other elements. –


uncertainty.

System ComplexityThe degree of analytical difficulty increasesexponentially as the number of interdependenciesand random variables increase.


System Performance Metrics• Metrics are measures used to assess the

performance of a system.• At the highest level of an organization or

business, metrics measure overall performancein terms of profits, revenues, cost relative tobudget, return on assets, and so on.


– Such metrics are inherently lagging, disguise low-level performance, and are reported only periodically

• From an operational standpoint, it is morebeneficial to track such factors as time, quality,quantity, efficiency, and utilization. – These operational metrics reflect immediate activity

and are directly controllable – They drive the higher financially related metrics.

Key operational metrics• Describe the effectiveness and efficiency of

manufacturing and service systems: – Flow time – Utilization


– - – Waiting time – Flow rate – Inventory or queue levels – Yield – Customer responsiveness – Variance



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System Variables• Designing a new system or improving an existing system

requires more than simply identifying the elements andperformance goals of the system.

• It requires an understanding of how system elementsaffect each other and overall performance objectives.

• Three types of system variable must be understand:


– Decision variables – Response variables – State variables

System Variables

• Decision variables – called as input factors or independent

variables – ’


independent variables affects the behavior ofthe system

– controllable or uncontrollable – controllable variable decision variables

Response Variables• Response variables

– called as performance or output variables – measure performance of the system in

response to particular decision variable


settings. – In an experiment, the response variable is the

dependent variable. – The goal in system planning is to find the right

values or settings of decision variables thatgive the desired response value.

State Variables

• State variables – State variables are the status of the system at

any specific point in time. –


state variable changes over time.

– State variables are dependent variables.

System Optimization• Optimization is finding the right setting for

decision variables that best meetsperformance objectives.

• O timization seeks the best combination


of decision variable values that eitherminimizes or maximizes some objectivefunctions such as costs or profits.

• An objective function is a responsevariable of the system.

System Optimization• A typical objective in an optimization

problem for a manufacturing or servicesystems:


– – maximizing flow rate

• Optimization problems may includeconstraints that limits the values ofdecision variables.



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System Optimization

• In some instances, there are problems ofconflicting objectives.


System Approach

• Due to departmentalization andspecialization, decisions in the real worldoften made without regard to overall


.• Approaching system design with overall

objectives in mind and considering howeach element relates to each other and tothe whole is called a systems or holisticapproach to system design.

System ApproachFour-step iterative approach to systems improvement


System Approach

• Identifying problems and opportunities – Developing a solution starts by understanding the

problem, identifying key variables, and describingimportant relationships.

– This hel s identif ossible areas of focus and


leverage points for applying a solution. – Techniques such as cause-and-effect analysis and

pareto analysis are useful. – Performance standards must be set high in order to

look for the greatest improvement opportunities. – Setting high standards pushes people to think

creatively and often results in breakthroughimprovement

System Approach• Developing alternative solutions

– Once a problem or opportunity has beenidentified and key decision variables isolated,alternative solution can be explored.


– This is where most of the design andengineering expertise comes into play.

– Generating alternative solutions requirescreatively as well as organizational andengineering skills.

– Simulation is particularly helpful in that itencourages thinking in radical new ways.

System Approach• Evaluating the solutions

– Alternative solutions should be evaluated based on their ability tomeet the criteria established for the evaluation.

– These criteria include performance goals, cost of implementation,impact on the socio-technical infrastructure, and consistencywith organizational strategies.


– Many of these criteria are difficult to measure in absolute terms. – After narrowing the list to two or three of the most promising

solutions using common sense and rough-cut analysis, moreprecise evaluation techniques may need to be used.

– This is where simulation and other formal analysis tools comeinto play.



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System Approach• Selecting and implementing the best solution

– Often the final selection of what solution to implementis not left to the analyst, but rather a managementdecision.

– The analyst ’ s role is to present his/her evaluation inthe clearest wa ossible so that an informed


decision can be made.

– Even after a solution is selected, additional modelingand analysis are often needed for fine-tuning thesolution.

– Implementers should then be careful to make surethat the system is implemented as designed,documenting reasons for any modifications.

System Analysis Techniques• While simulation is perhaps the most versatile

and powerful system analysis tool, otheravailable techniques also can be useful inplanning.

• These alternative techniques are usually


compu a ona me o s a wor we or s mp esystems with little interdependency andvariability.

• For more complex systems, these techniquesstill can provide rough estimates but fall short inproducing the insights and accurate answersthat simulation provides.

System Analysis TechniquesSimulation improves performance predictability


System Analysis Techniques

• In addition to simulation, system analysistools include: – Hand calculations


– – Operations Research techniques

System Analysis Techniques• Hand calculations

– Quick-and-dirty, pencil-and-paper sketches andcalculations can be remarkably helpful inunderstanding basic requirements for a system


– ome ecs ons may e so asc a a qu c men acalculation yields the needed results.

– Most of these calculations involve simple algebra. – The obvious drawback is the inability to manually

perform complex calculations or to take into accounttens or potentially even hundreds of complexrelationship simultaneously.

System Analysis Techniques• Spreadsheets

– What-if experiments can be run onspreadsheet based on expected values andsimple interactions.


– Spreadsheet simulation can be very usefulfor getting rough performance estimates.

– Weaknesses of spreadsheet modeling:• Some potential problems are not readily apparent• All behavior is assumed to be period-driven rather

than event-driven



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• OResearch Techniques – Traditional OR techniques utilize mathematical

models to solve problems. – These mathematical models include both

deterministic models and probabilistic models (e.g.



queuing and decision trees). – These OR techniques provide quick, quantitative

answers without going through the guessworkprocess of trial and error.

– OR techniques can be divided into: prescriptive anddescriptive


• Prescriptive techniques – an optimum solution to a problem – linear programming, dynamic programming – do not allow random variables – conditions are constant over the eriod of stud


• Descriptive techniques

– static analysis techniques such as queuing theory thatprovide good estimates for basic problems

– limited to only one or two metrics – give only average performance measures rather than

a complete picture of performance over time

QUEUING SYSTEM


QUEUING SYSTEM• Elements of queuing system

– Input source (calling population)• Size• Arrival distribution

– Queue


• engt

– Queuing disciplines

• FIFO, LIFO, priority – Service mechanism

• Number of service facilities• Structure of service facilities• Service distribution

QUEUING SYSTEM• Kendall ’ s notation

(a/b/c ) : (d/e/f )


a : Interarrival time distributionb : Service time distributionc : Number of parallel serversd : Service disciplinee : Maximum number of queue

f : Number of calling population

QUEUING SYSTEMFor a and b

M : Exponential distribution D : Degenerate distribution E k : Erlang distribution


G : General distribution for service time

Untuk disiplin pelayanan

FIFO : First-in, first out LIFO : Last-in, first outSIRO : Service in random order

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02 Introduction to Simulation