ExtendSim Tutorial

Dr David Brown

April 28, 2010

Innovative Decisions, Inc.

1945 Old Gallows Road

Suite 207

Vienna, VA 22182

www.innovativedecisions.comPhone:(703) 854-1130

Agenda

• ExtendSim Overview

• Continuous and Discrete Models

• Model Optimization

• Custom Model Blocks

• Animation

• Building Animation Objects

• Integration with Bayesian Networks.

What is ExtendSim?

• GUI based simulation package• Continuous, discrete event, discrete rate, and

agent-based modeling is supported• Robust set of modeling and data analysis

components. Allows for development of custom components.

• Links to external data sources and also includes built-in databases

• Supports animation and advanced graphic for enhanced presentation

Extend Libraries

Internal Block Settings

Activity Block

Double click to open block and set internal

options

D F

Typical Applications• Manufacturing

– Assembly lines, distribution, shipping

• Facilities

– Customer flow, operational efficiency, logistics

• Industrial Operations

– Construction, mining

• Military Operations

– Troop readiness, military logistics

• Process Improvement

– Business operations

Output

• Built-in graphing and report generation

• Export data to spreadsheets or databases

0

0.00005

0.0001

0.00015

0.0002

0.00025

20000 22000 24000 26000 28000 30000 32000 34000

Weekly Net Revenue ($)

Pro

bab

ilit

y

Baseline

Rule-based

Bayesian Network

Hierarchical Blocks

1 2 3

Rand

Assembly Station

Bins

InBins

Out

Sta#

Assembly Station

Bins

InBins

Out

Sta#Assembly Start

Bins

In

Bins

Out

Type

Bin In

1

Station #

Assembly Station

Bins

InBins

Out

Sta#

3

Station #Assembly Station

Bins

InBins

Out

Sta#

Assembly Station

Bins

InBins

Out

Sta#

Assembly Station

Bins

InBins

Out

Sta#

Assembly Station

Bins

InBins

Out

Sta#

6

Station #

Assembly Station

Bins

InBins

Out

Sta#

8

Station #

BinsInBinsIn

Assembly Station

Bins

InBins

Out

Sta#

Assembly Station

Bins

InBins

Out

Sta#

2

Station #

4

Station #

7

Station #

9

Station #

10

Station #

5

Station #

BinsOutBinsOut

D

T U

Bin Load Delay

select

b?

a

A

Get

BinTy pe a

b

L W

FD

Conv ey or Queue

BinsInBinsIn

BinsOutBinsOut

TCR

Receiv e

Load Delay Mean

1 2 3

Rand

Delay Time

StationInStationIn

3

Constant

TCR

Receiv e

Load Delay Dev

Eqn

1

Constant

N

B

A

Y a>b

TCR

Receiv e

# Stations

Activity-Based Costing

Types of Models

• Continuous Models

– Values are always present

– Examples: Temperature in a room, Accounts, Fuel in your car

• Discrete Models

– Things happen at specific times

– Examples: People enter a building, Cars in a parking lot, Files in an inbox

• Many models contain elements of each

– Example: Deposit to a savings account

Inputs and OutputsContinuous

BlockDiscrete

Block

ContinuousInput

ContinuousInput

ContinuousOutput

ContinuousOutput

DiscreteOutput

DiscreteInput

Outputs must beconnected to Inputs

y =f (x)

D F

Drag DownTabs

D

C

F

U

L

P

NB

SD

SH

PT

BT

DT

AS

Continuous Models

If only the value blocks are used, there are no special requirements

getHare Population

###1.25

Hare Birth Rate

Hare Births

Hare Pop

###100

Ecosy stem Area

Hares Killed

Lynx Pop

Lynx Pop

Hare Density

Hare Pop

Lynx Pop

getwant

Ly nx Population

###0.25

Ly nx Birth Rate

Ly nx Births

Value

Ly nx Mortality

Ly nx DeathsLynx Pop

Buttons

Discrete Models

ExecutiveBlock

An executive block must be used if any discrete blocks are used in a model.The executive block must be to the left of all other blocks.

demand

ADGate 1

startStart

Start

demand

ADGate 2

0

L

Stage 2

demand

ADGate 3

0

D F

Stage 3

L1

Indicator 1

1

L

Stage 1

L2

L4L1 L2

L4

L3

L3

conmax

Stage

A=0

A

B

Y

N

A=0

A

B

Y

N

A=2

A

B

Y

N

Indicator 1

C CO

Source

100

R Q

Valv e 1

Stage

800

C CO

I

Reception

T= 60

Indicator 2

Indicator 1

y =f (x)

%

ID

0

L

Stage 4

Indicator 2

Stage y =f (x)

0

R Q

Valv e 2

Stage

Stage 1: Open Valve 1 and release flow into Reception

Stage 2: Open Valve 2 and release flow into Processing

Stage 3: Process the flow

Stage 4: Open Valve 3 and release flow into Sink

0

C CO

I

Processing

4000

C CO

Recy cle

0

R Q

Valv e 3

y =f (x)

12000

C CO

Sink

Example Problem

• Work station for evaluation of incoming paperwork

– 1000 forms as input

• 100 per hour enter system

– Start with 3 workers

– One worker can review one form at a time

– Variable time to complete review

• Mean 3

• Std Dev 1

Work Flow FunctionsIncoming

Files

Workers

File

Review

Completed

Files

1000

100 per

hour

3

Mean 3

Std Dev 1

mins

1 file

1 worker

1 file

1 worker

Inbox

Work Flow Model

0

TR U

24

R L

0

0

Q

2

D F

i13

L

13

Incoming

Files

Workers

File

Review

Completed

Files

1000

100 per

hour

3

Mean 3

Std Dev 1

mins

1 file

1 worker

1 file

1 worker

Inbox

Run ExtendSim

Marine Corps Fuel and Water Logistics

• Modeled availability of complex fuel and water systems based on repair and transport of components

Marine Corps Fuel and Water Logistics

Included use, repair, procurement, transport, and use-life guidelines for multiple system components with different attributes.

Output was system availability data for multiple battalions with different system use profiles.

Navy Ship and Aircraft Operations

Ship and aircraft loading, movement, and debarkation modeling.

Naval Ship Design

Model movement of troops and vehicles within a new ship design to see how long it takes to outfit and deploy a landing force with their vehicles.

Model Optimization

• Extend can be used to optimize problems

• Mathematical solutions require equal numbers of variables and equations to solve

– These are not always available

• Extend uses genetic algorithms to optimize multiple variables in one equation

Optimization Problem

• An unprocessed file results in an average loss of $300

• A processed file results in a gain of $1000

• A worker costs $3000 per 600 time units

• What is the optimal number of people to assign to this job?

Work Flow Optimization

TR U

Workers

R L

Inbox

0

0

Q

D F

ir

L

Completed

585

Run ExtendSim

Set Optimization

Complex Optimization

Fire

FireCalls

EMS

EMS Calls

LE Calls

Full

Full

Dispatchers

Full

Non-emergency Calls

In progress

Completed

F

E

0

Dispatch

Operations

Simulation

In progress

Completed

F

L

0

Full

In progress

Completed

F

E

L 40

In progress

Completed

E

L0

In progress

Completed

F

23933

#

FEL

In progress

CompletedE

28699

In progress

CompletedL

31143

#

FE

Performance

Stats

Call

Processor(s)

Processing Input

Data

Target 0.95

#

FL

Target 0.99

#

ELPct of Calls < 15 secs

#

F

#

E

Pct of calls < 40 secs

#

L

Full

# of

processors

Dispatch Optimization

Modified Blocks

demand

a b cabc

a b cdemand

a b cabc

Standard

Block

Modified

Block

One IDI customer needed a

custom batching block that

could create custom orders

instead of batching the same

items every time

The blocks that come with

ExtendSim can be modified to

Meet unique customer needs

Other Custom Blocks

demand

a

b

c

demand

a

b

c

a b c

L W

FD

L W

F

use

changeu

#

I

use

changeu

#

change

use

u#

ichange

use

u# #

sensor

Gate

#

sensor

Gate

Variable unbatch

Buffer with variable input Bin with variable

starting items

Labor pool with variable

start laborersGate with variable number

Of items within the gate area

Animation

Advanced 3D visualization is supported.

Example Animation

Run ExtendSim

Integrating Netica Models with ExtendSim

Position

AirRouteReturnToForceNeither

20.211.568.3

Identity

HostileNeutralFriendly

42.025.532.5

RSM

F14Mig29BoeingAirbusOtherNone

30.824.39.257.255.4023.0

IFF

ValidReplyNoReply

5.6094.4

Comms

TrueFalse

33.966.1

Classification

Mig29MissileBoeingAirbusF14

30.05.0015.010.040.0

Kinematics

GT700ktss500to700KtsLT500kts

40.341.518.3

EW

F14RadarCommRadar1CommRadar2Mig29RadarMissileRadarNoRadar

27.011.713.221.57.7518.8

Intel

HostileNeutralFriendlyNone

18.415.116.550.0

Netica Models

• Netica is software package for the creation of Bayesian Networks and influence diagrams by Norsys Software Corp

• Comes in a graphical user interface version and a set of Application Programmer Interface (API) functions

Sample Network

Tuberculosis

presentabsent

1.0499.0

XRay Result

abnormalnormal

11.089.0

Tuberculosis or Cancer

truefalse

6.4893.5

Lung Cancer

presentabsent

5.5094.5

Dyspnea

presentabsent

43.656.4

Bronchitis

presentabsent

45.055.0

Visit To Asia

visitno visit

1.0099.0

Smoking

smokernon smoker

50.050.0

Distributed by Norsys Software Corp.

Contributing Factors

Diseases

Symptoms

Chest Clinic

Example Inference

Tuberculosis

presentabsent

6.7293.3

XRay Result

abnormalnormal

100 0

Tuberculosis or Cancer

truefalse

70.629.4

Lung Cancer

presentabsent

64.635.4

Dyspnea

presentabsent

73.226.8

Bronchitis

presentabsent

60.040.0

Visit To Asia

visitno visit

1.2298.8

Smoking

smokernon smoker

100 0

Distributed by Norsys Software Corp.

Contributing Factors

Diseases

Symptoms

Chest Clinic

Adding known information that the person is a smoker and that their chest

XRay was abnormal cause the probability of cancer to jump from 5% to 65%

Influence Diagrams

Weather

no rainrain

70.030.0

Satisfaction

Decide Umbrella

take itleave at home

35.000070.0000

Forecast

sunnycloudyrainy

53.521.525.0

UmbrellaDistributed by Norsys Software Corp.

A n Influence Diagram is a Bayesian Network that also includes

Decision and Utility nodes

Library Description

Get Belief

BeliefState#

The Netica library for Extend is a set of modeling blocks that allows you perform any function on a Netica Bayesian Network or Influence Diagramthat can be done using the Netica graphical user interface. Inputs an outputsallow for dynamic interaction with the rest of the Extend model.

Input desired

state number

Output belief

Why is this Unique?

Extend Netica API

ModL(Pascal-based)

C++(C-based)

Qu'est que c'est le croyanceque le noeud de rayons X estsur l'etat un?

Extend and Netica can not communicate with each other directly

What?

Custom Translator Allows Communications

Extend Netica API

ModL(Pascal-based)

C++(C-based)

Qu'est que c'est le croyance que le noeud de rayons Xest sur l'etat un?

.675

Translator(C++)

What is the belief that node XRay is in state one?

Applications for Netica Users

• Test a network you have created in its simulated operating environment

• Generate probabilities and utilities for CPT tables by having the network learn from a simulation

• Develop networks that can change over time• Batch process a large number of cases by reading

and writing to an Excel worksheet• Reduce complexity by building multiple, smaller

networks and then using Extend to have them operate as one

Applications for Extend Users

• Have networks make complex, multi-attribute decisions during the simulation

– Network can control the simulation

• Model a complex phenomenon that can not be described mathematically

Netica Library

Close Network

Initialize/Open

Network

Initialize/Open/

Compile Network

Open/ Compile

Network Sav e Network

File Functions

Network Functions

Enter Finding Enter Finding Not Enter Likelihood

1 2 3 4 5

Set Node Value

Value

Set State

State

Set State Not

State

Set Utility Value

Value

Enter/Set Functions

Get Belief

Belief

Get Belief

BeliefState#

Get Decision

Decision#

Get Likelihood

LikelihoodState#

Most Likely State

State

Most Likely Value

Value

Get Statistics

Mean Std

Dev

Get Value

Value

Get Functions

Compile Network Fade Node

degree

Retract All

FindingsRetract Node

Findings

Rev ise

Probabilities

degree

Select Network

Integrated Simulation Example

42

Radar

Equation Model

ExtendSim

Aircraft Motion

Equation Model

ExtendSim

Radar Cross Section

Bayesian Network

Netica

Shoot Decision

Influence Diagram

Netica

Aircraft Radar Cross Section

Bayesian Network RCS Model

0

5

10

15

20

25

30

35

dBsm

Test Data

Model Prediction

Bayesian network provides a good representation of the RCS

Radar Target Track(F-16 FMS Radar vs. B-29)

20

25

30

35

40

45

60 70 80 90 100 110 120 130

XRange (NM)

YR

an

ge (

NM

)

No Detection

Detection

Model displays target scintillation (fades in and out) like a real engagement

Combat ID Network

Position

AirRouteReturnToForceNeither

20.211.568.3

Identity

HostileNeutralFriendly

42.025.532.5

RSM

F14Mig29BoeingAirbusOtherNone

30.824.39.257.255.4023.0

IFF

ValidReplyNoReply

5.6094.4

Comms

TrueFalse

33.966.1

Classification

Mig29MissileBoeingAirbusF14

30.05.0015.010.040.0

Kinematics

GT700ktss500to700KtsLT500kts

40.341.518.3

EW

F14RadarCommRadar1CommRadar2Mig29RadarMissileRadarNoRadar

27.011.713.221.57.7518.8

Intel

HostileNeutralFriendlyNone

18.415.116.550.0

Courtesy of CDR George Laskey

Evaluation of Utility

Action/Hypothesis Friendly Commercial Hostile

Shoot - Destroy friendly

aircraft.

- Damage US

prestige.

- Tens of million of

dollars in damage

and loss of pilot

- Possible loss of job

- Destroy

commercial airliner

and kill possibly

hundreds of civilians

and cause tens of

million of dollars in

damage

- No harm to US

forces but significant

damage to US

prestige.

- Destroy enemy

aircraft.

- Reaffirms US

forces are capable of

defending

themselves.

Don’t Shoot - No harm to US

forces or prestige.

- No harm to US

forces or prestige

- Significant damage

to US fleet and

prestige on the order

of hundreds to

thousands of lives

lost and hundreds of

millions to billions $

in damage

47

Expert Elicitation Network Test

0

50

100

150

200

250

MIG-29 Missile Boeing Airbus F-14

Aircraft

En

ga

ge

me

nts

Shoot

No_Shoot

49

Network Modification• Network was then modified by adding arcs from node

“Identity” to nodes “EW” and “Kinematics”. • A new policy that a MIG-29 flying at the radar at speeds

greater than 500 knots and with their radar on is considered to be hostile. Otherwise, these aircraft are neutral.

• The influence diagram was cleared of all probabilities and utility values.

• The equation-based model generated random aircraft engagements, but was modified to randomly make the shoot decision and then determine the utility of that decision.

• Once all node states for the influence diagram were set, the probabilities and utility values were updated

Simulation Learning Network Test

0

50

100

150

200

250

MIG-29 Missile Boeing Airbus F-14

Aircraft

En

ga

ge

me

nts

Shoot

No_Shoot

Genetic Agents Teach an Influence Diagram

count

OpEMCSS

CSS

Priority

Initialize

B

Def ine Rewards

Ev olv e

Agent2 Ev olv e

Local

Agent1 Reward

Local

Agent2 Reward

Logic

AssembleSplit

Initialize/Open/

Compile Network

Game

Set Node Value

Value

Agent1

A

Get

Agent1 Mov e

A

Get

Agent2 Mov e

Statistics

EDef ine MOEs

Set Node Value

Value

Agent2

Set Utility Value

Value

Utility

Reward1 Reward2

Mov e Statistics

Agent1 Mov e Agent2 Mov e

Local

Agent1 Best

Ev olv e

Agent1 Ev olv e

Local

Agent2 Best

Set Game ParametersFirst Move

Multiple Games to Find Best Strategy

Results

Current Game Results

Rev ise

Probabilities

degree

degree

Sav e Network

Game2

A

Set A

degree

Agent1

Agent2

a

b

Close Network

Agent1

Agent2

A

Set A

A

Set A

A

Set A

A

Set A

?