W5 - Model Development

8/19/2019 W5 - Model Development

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Lab. KOI Jurusan Teknik Industri - ITS

Pre-eliminary activities

Contents

Simulation Procedure

123456

Software Selection

Personal Identification

Selecting an Application

See Chapter One Why When What Simulation

Defining the Objective

Defining the Scope Of Work

- Model Scope

- Level Of Detail

- Data Gathering Responsibilities

- Planning Experimentation

- Form of results

Defining Project Requirements

Pitfalls Why simulation projects fail


Preliminary Activities

The decision to use simulation itself requiressome consideration. Is the applicationappropriate for simulation? Are otherapproaches equally as effective yet lessexpensive? Hence we should know WWW

of simulation, what, why, when.

(see chapter one presentation)


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1. Selecting an Application

• Is the process well defined?

• Is process information readily available?

• Does the process have interdependencies?

• Does the process exhibit variability?

• Are the potential cost savings greater than the cost of

doing the study?

• If it is a new process, is there time to perform a simulation

analysis?

• If it is an existing process, would it be less costly to

experiment on the actual system?

• Is management willing to support the project?



2. Personal identification

– Management assign a project leader who works alone

or with a project team

– It may be more cost effective to hire a simulation

consultant than to do the simulation in-house

– A simulation specialist should be assigned to lead and

an engineer (manager) who works closely with thespecialist should be assigned

– Personnel must be identified and trained

– The process owner should take an active role in the

project


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3. Software selection

– Simulation software can’t make a simulation succeed, but

it can make it fail

– Selecting the right simulation software requires that an

assessment first be made to the simulation requirements.

– Alternative products should be evaluated against those

requirements

– Selection criteria: quality, features and capability, ease

of use, services, and cost


Simulation SoftwareSoftware Supplier

Arena Rockwell Software

AutoMod Brooks-PRI Automation

Awe Sim Frontstep, Inc.

Enterprise Dynamics Incontrol Enterprise Dynamics

Extend Imagine That, Inc

Flexsim Flexsim Software Products, Inc.

GPSS/H Wolverine Software Corporation

Micro Saint Micro Analysis and Design

ProModel (MedModel, ServiceModel) ProModel Corporation

Quest DELMIA Corporation

ShowFlow Webb Systems Limited

SIGMA Custom Simulation

Simprocess CACI Products Company

Simul8 Visual8 Corporation

SLX Wolverine Software Corporation

Visual Simulation Environment Orca Computer, Inc.

Witness Lanner Group, Inc.


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Simulation Software

Feature SpreadsheetProgramming

language

Specialist simulation

software

Range of application Low High Medium

Modeling flexibility Low High Medium

Duration of model build Medium Long Short

Ease of use Medium Low High

Ease of model validation Medium Low High

Run-speed Low High Medium

Time to obtain software skills Short (medium for

macro use)

Long Medium

Price Low Low High



Step 1 : Define objective, scope, and requirements

Step 2 : Collect and analyze system data

Step 3 : Build the model

Step 4 : Validate the model

Step 5 : Conduct experiments

Step 6 : Present the result

(Each step need not be completed in its entirely before moving to

the next step)


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Present result

Collect and analyze system data

Build model

Validate model

Conduct experiment

Define objective, scope, and requirements


Defining The Objective

• The objective of a simulation defines the purpose

or reason for conducting the simulation study.

• It should be realistic and achievable, given the

time and resource constraints of the study


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• Simulation objectives can be grouped into the

following general categories :

• Performance analysis – What is the all-around performance of

the system in terms of resource utilization, flow time, output

rate, and so on?

• Capacity/constraint analysis – When pushed to the maximum, what

is the processing or production capacity of the system and

where are the bottlenecks?

• Configuration comparison – How well does one system

configuration meet performance objectives compared toanother?



• Optimization – What setting for particular decision

variables best achieve desired performance goals ?

• Sensitivity analysis - Which decision variables are

the most influential on performance measures, and

how influential are they?

• Visualization – How can system dynamics be most

effectively visualized?


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Design Decisions

What are the optimum number and size of waiting areas, storage

areas, queues, and buffers?

What is the optimum unit load or batch size for processing?

Where are the bottlenecks in the system, and how can they be

eliminated?

Operational Decisions

What is the best schedule for preventive maintenance?

What is the best priority rule for selecting the jobs and tasks?

How many personnel should be scheduled for a particular shift?

Examples of Specifics Objectives


Test Yourself

For each of the following systems, define one possible objective:

A manufacturing cell

A fast-food restaurant

An emergency room of a hospital

A tire distribution center

A public bus transportation system

A post office

A rental car agency at a major airport


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• It is always best if objectives can be clearly,

completely, and precisely stated.

• To be effective, an objective should be one that

– Has high potential impact

– Is achievable

– Is specific

– Is quantifiable

– Is measurable – Identifies any relevant constraints


Defining the Scope of Work

• The scope of work is important for guiding the study

as well as providing a specification of the work to be

done upon which all can agree

• An important part of the scope is a specification of

the models that will be built. When evaluating

improvements to an existing system, it is often

desirable to model the current system first. This is

called an “ as-is” model.

• As-is model results are statistically compared with

outputs of the real-world system to validate the

simulation model


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• As-is model can be used as a benchmark or baseline

to compare the result of“to-be” models.

• To ensure that the budget and schedule are realistic,

a detailed specification should be drafted to include:

– Model scope

– Level of detail

– Data-gathering responsibilities

– Experimentation

– Form of results



• Determining Model Scope

• Model scope refers to what system elements should be

represent in the model

• Determining the scope of a model should be based on

how much impact a particular activity has on achieving the

objectives of the simulation

• Decision regarding model scope relates to the modelboundaries

• A common tendency for beginners is to model the entire

system, even when the problem area and all relevant

variables are actually isolated within a smaller subsystem


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• In addition to limiting the model boundaries, other

system elements may be excluded from a model due totheir relevance.

• The determination of what elements can be eliminated

should be based on their relevance to the objectives of

the study

Activity

AActivity B Activity C

Activity

D

Activity E

Scope of model



• Deciding on Level of Detail

– The level of detail determines the depth of a model.

– Real world system can be modeled as a single “black

box” or “white box” model

– Too much detail makes it difficult and time-consuming to

develop and debug the model. Too little detail may make

the model unrealistic by oversimplifying the process.

– Level of detail is determined largely by the degree of

accuracy required in the result


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Model development time

One-to-onecorrespondence

Minimumrequired

L e

v e l o f

d e t a i l



• Assigning Data – Gathering Responsibilities

– In any simulation project, data gathering is almost always

the most difficult and time-consuming task.

– Identifying data requirements and who will be responsible

for gathering the data is essential if this activity is to

succeed

– If the modeler is responsible for gathering data, it isimportant to have the full cooperation of those who

possess the data, including equipment vendors, engineers,

technicians, direct labor personnel, and supervisors.


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• Planning the Experimentation

• The basic premise is that you are not ready to improve a

system until you understand how the current system

operates.

• Once a model of the current system is built, it is easier to

visualize what changes need to be made for the modified

system. Both system may even be modeled together in the

same simulation and made to run side by side

• The number and nature of the scenarios or alternative

configurations is influenced by the time constraints of thestudy



Determining the Form of Result

The form of results to be presented can significantly affect

the time and effort involved in the simulation study

The key to determining the kind and quantity of information

to present to the decision maker or stakeholder is to ask

what decision is being made with the simulation and what

the background is of the person(s) who will be making thedecision.

Simulation animation can effectively compare alternative

scenarios.


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• Resource, budget, and time requirements can be

determined for the project with the scope of work

defined

• The primary task at this point is to develop a budget

and schedule for the project.

• The time to perform a simulation project will vary

depending on the size and difficulty of the project



A simulation schedule should be based on realistic

projections of the time requirements, keeping in mind that:

• data gathering and analysis can take over 50 percent of theoverall project time

• model building usually takes the least amount of time (10 to 20percent)

• once a base model is built, several additional weeks may be

needed to conduct all of the desired experiments, especially if thepurpose of the project is to make system comparisons

• At least several days must be allowed to clean up the model and

develop the final presentation• Simulation project follow the 90 – 10 rule, where the first 90 percent

takes 10 percent of the time, and the last 10 percent takes theother 90 percent


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Reasons Why Simulation Projects Fail

• Unclear objectives

• Unskilled modelers

• Unavailable data

• Unmanaged expectations

• Unsupportive management

• Underestimated requirements

• Uninvolved process owner(s)

The best way to ensure success is to make sure that everyone

involved is educated in the process and understands the benefits and

limitations of simulation



Present result


Build model

Validate model

Conduct experiment


Discussed in a separate chapter


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Present result


Build model

Validate model

Conduct experiment



Simulation Studies

Key modelling process:• A conceptual model; a description of the model that

is to be developed

• A computer model; the simulation modelimplemented on a computer

•Solutions and/or understanding; derived from theresult of the experimentation

• An improvement in the real world ; obtained fromimplementing the solutions and/or understandinggained


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Simulation Studies – Build Model

Real world

(problem)

Conceptual

model

Solutions/

understanding

Computermodel


Simulation Studies– Build Model

• Conceptual modeling

Develop an understanding of the problem

situation

Determine the modeling objectives

Design the conceptual model: inputs, outputs,

and model content

Collect and analyze the data required to

develop the model


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Simulation Studies – Build Model

• Model coding In model coding the conceptual model is

converted in to a computer model

Coding is defined in its most general sense and

does not strictly mean computer programming

The assumption here is that the simulation is

built and performed on a computer


Simulation Studies– Build Model

• Experimentation Experiments are performed with the simulation

model in order to obtain a betterunderstanding of the real world and/or to findsolutions to real world problems

It is a process of “what–if” analysis, that is,

making changes to the model’s inputs, runningthe model, inspecting the result, learning fromthe results, making changes to the inputs and soon


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Conceptual Modeling

The conceptual model is a non-software specific

description of the simulation model that is to be

developed, describing the objectives, inputs,

outputs, content, assumptions and simplifications of

the model


Conceptual Modeling

• Objectives: the components that are represented inthe model and their interconnections

• Assumptions made either when there areuncertainties or beliefs about the real world beingmodeled

• Simplifications incorporated in the model to enable

more rapid model development and use (ways ofreducing the complexity of the model)


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Conceptual Modeling

The content of the model should be described in terms of

two dimensions (Robinson, 1994):

The scope of the model : the model boundary or

the breadth of the real system that is to be

included in the model

The level of detail : the detail to be included for

each component in the model’s scope


Conceptual ModelingRequirements of the conceptual model:

1. Validity : a perception, on behalf of the modeler, that theconceptual model will lead to a computer model that issufficiently accurate for the purpose at hand

2. Credibility : a perception, on behalf of the clients, that theconceptual model will lead to a computer model that issufficiently accurate for the purpose at hand

3. Utility : a perception, on behalf of the modeler and theclients, that the conceptual model will lead to a computermodel that is useful as an aid to decision-making within thespecified context

4. Feasibility : a perception, on behalf of the modeler andclients, that the conceptual model can be modeled


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Representing the Conceptual Modeling

• Component list

• Process flow diagram

• Logic flow diagram

• Activity cycle diagram

• Petri nets

• Event graphs

• Condition specification

• The Unified Modelling Language (UML)• Rich picture (Soft System Methodology)


Component List

• A list of the components in the model with some

description of the detail included for each

• It does not provide a visual representation of the

conceptual model and it is difficult to capture

complex logic and the process flow


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Component List

• Example: component list for Single Server Queue

Component Detail

Customers Time between arrivals (distribution)

Queue Capacity

Service desk Service time (distribution)


Process Flow Diagram

• The conceptual model is represented as a processflow or process map, showing each component ofthe system in a sequence and including somedescription of the model detail

• A process might be shown as a box and a queue asa circle

• It is still difficult to capture more complex logic


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Process Flow Diagram

• Example: Process Flow Diagram for Single Server Queue

Customers(interarrival time)

Queue(capacity)

Service(service time distribution)


Logic Flow Diagram

• It uses standard flow diagram symbols to represent

the logic of the model rather than the process flow

• The process flow is not always obvious, however,

these diagrams can quickly become large, complex

and cumbersome for models of any reasonable

scale


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Logic Flow Diagram

• Example: Logic Flow

Diagram for Single

Server Queue

Customer Arrives

Space in queue?

Queue for service

Server available?

Customer served

Customer leaves

No

No


Activity Cycle Diagram

• ACD are used as a specific means for representing

discrete-event simulation models (Hill, 1971)

• Circles represent dead states, where an item waits

for something to happen

• Rectangles represent active states, where an item is

acted upon


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• ACD provide a convenient means for identifying the

events in a simulation and so their main use hasbeen to act as a basis for programming simulationmodels

• ACD are probably less useful if a simulationpackage is to be employed



The basic conventions for drawing an ACD are as

follows:

1. Each entity and resource has an activity cycle.

2. Each cycle consists of activities and queues.

3. Activities and queues alternate in a cycle.

4. Activities are depicted by rectangles andqueues by circles.

5. A cycle is closed.


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• Example: ACD for Single Server Queue

Customer arrival Service

Queue

Outside




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Relationships between ACD, the activityorientation and process orientation


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Building Arena from ACD




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Arena Simulation and Output Reports


Arena Simulation and Output Reports


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Documents

W5 - Model Development