Computer Simulation (Introduction)

Computer SimulationIEG3L3

RYP

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General Information

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Le Task

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Learning Objective1. Students are able to formulate dynamic

system problem into simulation model. 2. Students are able to create a simulation

model using ProModel and Matlab as a software.

3. Students are able to perform verification and validation of simulation models.

4. Students are able to analyze alternative solutions obtained by the simulation model.

Reference

1. Charles Harrell, Biman K. Ghosh, Royce O. Bowden, Simulation using ProModel, McGraw-Hill, 2011.

2. Averill M. Law, Simulation Modeling and Analysis, McGraw-Hill, 2011.

3. Matlab

Scoring Component

Midterm : 25 % Final Exam : 25 % Quiz : 5 % Task : 25 % Labwork : 20 %

The SAP1 Introduction to Simulation

2 System Dynamics

3 Simulation Basic 1

4 Simulation Basic 2

5 Monte Carlo Simulation

6 Discrete Event Simulation

7 Simulation Procedure

8 Midterm

The SAP9 Data Collection & Analysis

10 Model Building

11 Model Verification and Validation

12 Simulation Output Analysis

13 Comparing Systems

14 Basic Simulation Optimization with Matlab

15 Advance Simulation Optimization with Matlab

16 Final Exam

Outline

What is simulation?

Why simulate?

When simulation is appropriate

Use of simulation

Characteristic of simulation

Doing simulation

Qualification for doing simulation

Types of simulation

Advantage, Disadvantage, and Pitfall of Simulation

What is simulation?

• The Oxford American Dictionary (1980) defines simulation as a way “to reproduce the conditions of a situation, as by means of model, for study or testing or training, etc.”

• According to Schriber (1987), simulation is “the modeling of process or system in such a way that the model mimics the response of actual system to events that take place over time.

What is simulation?

• Simulation is the imitation of dynamic system using a computer model in order to evaluate and improve system performance.

What is System?

Set of elements or components or subsystems with integrated each other and interact to achieve a certain goal.

System

Illustrasi

Body System:Head

SubsystemBody

SubsystemArm

Subsystem

Illustrasi 2

Computer System: Software

Subsystem Hardware

Subsystem Brainware

Subsytem

System Concept

Input Process Output

What is Model?

Model?

FYI :Model is one of traditional food from Palembang. Model is in family of Pempek

Model

A model is a simplified representation of reality, with emphasis on the word simplified

The power of a model is more a function of its simplicity rather than its complexity

Why simulate?

• Expensive• Time-consuming• Disruptive

Trial- and-error

approaches are:

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 traditional trial-and-error techniques.

• The power of simulation lies in the fact that it provides a method of analysis that is not only formal and predictive, but is capable of accurately predicting the performance of a system.

• By using a computer to model a system before it is built or to test operating policies before they are actually implemented, many of the pitfalls can be avoided.

When simulation is Appropriate

Simulation is often used:

no suitable theoretical model exists

the problem is so complex that a theoretical model cannot represent the interrelationships properly

When simulation is Appropriate

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

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

• Simulation has certain limitations of which one should be aware before making a decision to apply it to a given situation.

• As a general guideline, simulation is appropriate if:– An operational (logical or quantitative) decision is being made.– The process being analyzed is well defined and repetitive.– 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.

Use of simulation• Typical applications

of simulation include– Work-flow planning– Capacity planning– Cycle time reduction– Staff and resource

planning– Work prioritization– Bottleneck analysis– Quality improvement– Cost reduction

Inventory reduction

Throughput analysis

Productivity improvement

Layout analysis

Line balancing

Batch size optimization

Production scheduling

Resource scheduling

Maintenance scheduling

Control system design

Characteristics of Simulation

• Capture 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• Provides information on multiple performance

measures• Is visually appealing and engages people interest• Provides result that are easy to understand an

communicate• Runs in compressed, real, or even delayed time• Forces attention to detail in a design

Doing SimulationSimulation provides a virtual method for doing

system experimentation.

Doing Simulation

Drawing conclusions about the

validity of the hypothesis

Testing the hypothesis

through experimentatio

n

Setting up an experiment

Formulating a hypothesis

Doing Simulation

STARTSTART FORMULATE A HYPOTHESISFORMULATE A HYPOTHESIS

DEVELOP A SIMULATION MODEL

DEVELOP A SIMULATION MODEL

RESUME A SIMULATION EXPERIMENT

RESUME A SIMULATION EXPERIMENT

HYPOTHESIS CORRECT?

HYPOTHESIS CORRECT?STOPSTOP YESYES NONO

Qualification for doing Simulation

To reap the greatest benefit from simulation, a certain degree of knowledge and skill in following areas is useful– Project management– Communication– System engineering– Statistical analysis and design of experiments– Modeling principles and concept – Basic programing and computer skills– Training on one or more simulation product– Familiarity with the system being investigated

Types of Simulation

• Continuous Simulation• Combined Discrete-Continuous Simulation• Monte Carlo Simulation• Spreadsheet Simulation

Continuous Simulation

• Concerns the modelling over time of a system by a representation in which the state variables change continuously with respect to time.

• Involve differential equations that give relationship for the rates of change of the state variables with time.

• Software : SIMULINK,ACSL,Dymola

Combined Discrete-Continuous Simulation

• The need to construct a model with aspect of both discrete-event and continuous simulation, resulting in a combined discrete-continuous simulation.

• Pritsker describes the three fundamental type of interaction that can occur between discretely changing and continuously changing state variables :– A discrete event may cause a discrete change in the

value of a continuous state variable.– A discrete event may cause the relationship governing a

continuous state variable to change at particular time– A continuous state variable achieving a threshold value

may cause a discrete event to occur or to be scheduled.

Monte Carlo Simulation

• Is a scheme employing random numbers which used for solving certain stochastic or deterministic problem.

• This method was originated during World War II to applied problem related to the development of the atomic bomb.

Spreadsheet Simulation

• Discrete-event and monte carlo can sometimes be done in spreadsheets such as excel if the problem of interest is not too complex.

• However spreadsheets have the following important limitation :– Only simple data structures are available– Complex algorithms are difficult to implement– Spreadsheet simulations may have longer execution times than

simulations built in a discrete-event simulation package.– Data storage is limited.

• Spreadsheet simulation are widely used for performing risk analyses in application areas such as finance, manufacturing, project management, and oil-gas discovery.

Advantage of Simulation

• Most complex system cannot be accurately described by a mathematical model that can be evaluated analytically, a simulation is possible to do the thing

• Simulation allows one to estimate the performance of an existing system under some projected set of operating conditions

• Alternative proposed system designs can be compared via simulation to see which best meets a specified requirement

• In a simulation we can maintain much better control over experimental condition that would generally be possible when experimenting with the system itself.

• Simulation allow us to study a system with a long time frame.

Disadvantage of Simulation

• Each run of stochastic simulation model produces only estimates a model true characteristics for a particular set of input parameters.

• Simulation models are often expensive and time-consuming to develop

• The large volume of number produced by a simulation study or the persuasive impact of realistic animation. If a model is not a valid representation of a system under study, the simulation result no matter how impressive they appear will provide little useful information about the actual system

Pitfall of Simulation• Failure to have well-defined set of objective at the beginning of the

simulation study• Failure to have the entire project team involved at the beginning of the

study• Inappropriate level of detail• Failure to communicate with management throughout the course of the

simulation study• Misunderstanding of simulation by management• Treating a simulation study as if it were primarily an exercise in computer

programming• Failure to have people with a knowledge of simulation methodology and

statistics on the modeling team• Failure to collect good system data• Inappropriate simulation software

Pitfall of Simulation• Obliviously using simulation-software products whose complex

macro statements may not be well documented and may not implement the desired modeling logic

• Belief that easy to use simulation packages, which require little or no programming

• Misuse of animation• Failure to account correctly for sources of randomness in the

actual system• Using arbitrary distributions as input to the simulation• Analyzing the output data from one simulation run using

formulas• Making a single replication of a particular system design and

treating the output statistics as true answer• Failure to have a warmup period • Comparing alternative system design on the basis of one

replication for each design• Using the wrong performance measure

Simulation as an OR/MS tool

Simulation as an OR/MS tool

Simulation is one of the most spectacular MS/OR modeling techniques.

But sometimes, the results of effort spent on simulation are disappointing, can be because excessive concentration on the modeling phase with too little thought and effort spent on problem formulation and implementation.

What aspects tend to go wrong?

What aspects tend to go wrong in simulation modeling?

1. Selecting simulation because you don’t know what else to do.Simulation, especially computer simulation, is sometimes described as a last-resort technique, to be used when attempts to fit some kind of analytical model have failed.This is so wrong, because simulation is a technique that may require:

– large inputs– data collection– study and understanding of the system being

modeled– knowledge of the simulation software– knowledge of the special statistical problems

that occur in the analysis of simulation output.


2. Poor initial planningThis can lead to time completion of the model becoming grossly underestimated.Two major reasons for poor initial planning are:

– Premature coding: The irresistible urge to begin coding before the system and the problem are properly defined.

– Optimistic or lack of realistic scheduling: Underestimating the time required for known tasks, such as problem scoping and data collection, and neglecting to allocate time for the inevitable, unanticipated problems.


3. Failure to define achievable goals or objectives

Modeling is not a goal; it is a means of achieving a goal. We need a set of realizable objectives, and cannot be correctly defined without the participation of the problem owner.

4. Wrong simulation softwareGeneral-purpose languages such as Visual Basic will be useful for simple simulation program. However, for a large model it is much easier to use a simulation package that provide features like complex control of the flow of events, and reliable random variate generation, it can reduce both programming and project time.


5. Incomplete mix of essential skillsA successful simulation project calls for a combination of at least four areas of knowledge and experience:

– Project leadership: The ability to motivate, lead and manage the simulation team.

– Modeling: The ability to design a conceptual model that imitates the system under study at the required level of detail.

– Programming: The ability to transform the model into a logical, modifiable, working computer program.

– Knowledge of the modeled system: Sufficient understanding of the system to guide the modeling and to judge the validity of the simulation results.

Teams with lack of knowledge and experience combination are tend to fail.


6. Inadequate level of user participation

– The model-building team must work with the intended users.– There should be regularly scheduled briefings, progress

reports, and technical discussions with the users. – Users can inform the team about realistic considerations, such

as politics, bureaucracy, unions, budget limitations, and changes in the sponsoring organization.

7. Inappropriate level of detail

– There is no need to spend a great deal of effort modeling in unnecessary detail.

– A model is a simplified representation of a system, and it should incorporate only those features of the system thought to be important for the users’ purpose.


8. Obsolete or nonexistent documentation

– Many unsuccessful simulation projects end up with no documentation .\

9. Using an unverified or invalid model

– Verification (‘internal validation’) involves comparing the programmed computer model with the system model.

– Validation (‘external validation’) involves comparing the system model with the real world.

10. Poor presentation of results

– The results from simulation studies are often presented in a way that the user finds incomprehensible.

Le Task Numero 1

• Make a Group consist of 4 People• Create a Poster with contents below :

– Definition of Simulation– Why use Simulation– Type of Simulation– Example of Simulation (with study case)

Send the Poster to my Google Drive Today before 24.00 WIBPut it on Task FolderSubject of the task : IEG3L3_WEEK1_NIM1_NIMXBest 3 Design and Content will be given extra point and must presentated it next week

Computer Simulation (Introduction)

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