multi-paradigm simulation innovations for manufacturing ...with particular relevance to systems of...

Multi-Paradigm Simulation

Innovations for Manufacturing

Enterprises

Dr. Young-Jun Son

Professor, Systems and Industrial Engineering

The University of Arizona

[email protected]; 1-520-626-9530

http://www.sie.arizona.edu/faculty/son/index.html

Director

Center for Advanced Integration of Manufacturing Systems and Technologies (AIMST)

The University of Arizona

AME Meeting on April 14th, 2011

mailto:[email protected]

http://www.sie.arizona.edu/faculty/son/index.html

Computer Integrated Manufacturing & Simulation Lab

Department of Systems and Industrial Engineering, The University of Arizona, Tucson

Agenda

• (Education) SIE Department of The University

of Arizona

• (Research and Practice) Simulation

innovations

Processes

Management

System Employee

Engagement



Mission

• Provide a basis for lifelong learning of the engineering and scientific knowledge required for analysis, design, improvement, and evaluation of integrated systems of people, computers, material, and equipment.

• Lead in research in key areas within the discipline, with particular relevance to systems of strategic importance to the state and the nation such as manufacturing, telecommunications, transportation, health-care, service, and environmental systems.

• Provide professional service and academic leadership in the disciplines of the Systems and Industrial Engineering Department, within the university, industries, governmental agencies, private and public organizations, and professional societies.

The World’s First

Systems Engineering

Program

It is our mission to prepare students with the knowledge and skills they need to design, model, analyze and manage modern complex systems. Our courses, laboratories, projects and seminars prepare students for rewarding engineering careers and life-long learning.



Faculty

• SIE Faculty – Bahill, A. Terry, Professor

– Bayraksan, Güzin, Assistant Professor

– Furfaro, Roberto, Assistant Profesor

– Goldberg, Jeffrey B., Dean

– Head, K. Larry, Associate Professor and Department Head

– Lin, Wei H., Associate Professor

– Liu, Jian, Assistant Professor

– Son, Young Jun, Professor

– Szidarovszky, Ferenc, Professor

– Wang, Feiyue, Professor

• Joint Faculty – Dror, Moshe, Professor,

Management Information Systems

– Hickman, Mark, Associate Professor, Civil Engineering and Engineering Mechanics

• Engineering Management Faculty

– Arnold, Michael, Professor-of-Practice

– Geiger, Gordon, Emeritus Professor

– Hunter, Jane, Adjunct Assistant Professor

• Visiting/Research Faculty – Esra Buyuktahtakin, Visiting Assistant Professor

• Adjunct Professors/Instructors

– Rashid Al Jalahema, Instructor

– Bakken, Gary, Adjunct Associate Professor

• Emeritus Professors – Bob Baker

– Duane Dietrich

– Russ Ferrell

– Marcel Neuts

– John Ramberg

– Don Schultz

– Suvrajeet Sen

– Wayne Wymore

– Sid Yakowitz


Department of Systems and Industrial Engineering, The University of Arizona, Tucson 5

A VISION for SIE & EMG

Systems Engineering

Engineering

Management

Industrial

Engineering

Understand Business & Technology

Public Policy & Administration

Legal Issues of Technology

Globalization

Customerization: Buyer-centric

Manufacturing. New

Manufacturing and Production

Methods, Supply Chain &

Logistics

Structured Methodologies to

Integrate Components &

Technologies in Large

Complex, Networked Systems


Department of Systems and Industrial Engineering, The University of Arizona, Tucson 6

Signature Research Themes • Methodologies (Analytics)

– The Systems Engineering Process

– Modeling and Simulation

– Probability and Statistics

– Optimization (Large-Scale, Stochastic)

– Game Theory/ Decision Making Under Uncertainty

– Production Planning and Control

– Quality and Reliability

– Dynamics and Control

– Software Engineering

– Finance & Economic Planning

– Engineering Business Management Principles

– Technical Sales & Marketing

– Technology Development and Transfer

• Applications – Manufacturing Systems

• Production, Logistics & Extended Enterprise

– Transportation Systems

• Real-Time Traffic Management/Evacuation Planning

• Traffic Flow Theory/Simulation

– Energy & Natural Resources Management

• Water Resources

• Transmission Networks

• Generation and Capacity Planning

– Health Care Systems

• Emergency Response Planning/Operations

• Blood Supply Network Operations

– Bio-Systems

• Human Decision Making

• Systems Biology

– Homeland Security

• International Harbor Simulation and Security

• Pedestrian Crowd Simulation Under Attack

• Risk Analysis of Border Security & Immigration

– Economics and Management

• Market Engineering

• Management of Engineering and Technology

- Space Systems Engineering

• Mission Design

• Guidance, Navigation, and Control



Distance Learning Opportunity

• MS and MENG

• Real-time class participation via Polycom

(RMX 2000)

• Lectures archived (Accordance)

• Many remote students (e.g. Toyota Technical

Institute in Japan; Raytheon; Boeing; Sandia)

• http://www.sie.arizona.edu for more details

http://www.sie.arizona.edu/



Son’s Research Group (Members)

• Members: Son; 1 post-doc; 8 PhD students; 1 MS student

• Publications: Over 100 papers (journal/proceedings) last 15 years

• Sponsors: NSF, NIST, AFOSR, DOT, SFAz, Sandia, Raytheon,

Boeing, Samsung, Motorola, Peobody, APS, TEP

Three

additional

students



Son’s Research Group (Awards) • Group

– IERC Best Paper Award – Modeling and Simulation (2005)

– IERC Best Paper Award – Homeland Security (2008)

– IERC Best Paper Award – Modeling and Simulation(2009)

• Faculty

– SME 2004 Outstanding Young Manufacturing Engineer Award

– IIE 2005 Outstanding Young Industrial Engineer Award

– Best paper in IJIE (2007)

– Editor-in-chief and editorial board (8 international journals)

• Graduate students

– Best MS thesis award (2011)

– Best Ph.D. Scientific poster award in IERC (2008)

– Best Ph.D. Scientific poster award in IERC (2009)

– Best MS thesis award (2009)

– ICIE 2003 Graduate Student Paper Contest (first place)

– ICIE 2003 Graduate Student Paper Contest (third place)

• Undergraduate students

– IIE/Rockwell Software National Student Simulation Competition (first place in

2002, second place in 2004; third place in 2001 and 2006)



Enabling (Foundational) Methodologies and Technologies

Multi-paradigm

simulations

(DES, SD, AB,

CAVE-VR)

Multi-scale

simulation and

decision

models

Computer

science

foundation

Applications: Large-scale dynamic systems (systems of systems), where

synchronization across space and time is important

Time and

information

synchronization

Distributed

computing

(Web services,

HLA/RTI,

Grid computing)

Research Overview

From shop

floor to top

floor

(NIST,

NSF-DDDAS)

(Emerging)

Emergency

evacuation

(NIST,

Air Force)

(Emerging)

Homeland

security

(Sandia Labs)

(Emerging)

Network of

stakeholders

in software

enterprise

(NSF-SOD)

(Emerging)

Blood supply

network

(Red Cross,

CreateASoft,

IBM)

Research Theme:

Distributed federation of simulation

and optimization models, hardware and software components



Research Presentation Agenda

• Major sponsored projects in multi-paradigm simulation innovations (state-of-the-art) – Simulation-based planning and control (NIST; NSF)

– Extension to top level activities (NIST)

– Synthetic human decision-behavior model (AFOSR; DOT)

• Evacuation under factory fire (layout design)

• Workforce assignment (NSF)

• Simulation usage

– Agent-based modeling

– CAVE-based virtual reality for data collection of human behaviors

– Interoperability among heterogeneous manufacturing applications



Simulation-based Planning and Control

Y. Son, S. Joshi, R. Wysk, and J. Smith, Simulation Based Shop Floor Control, Journal of Manufacturing

Systems, 21(5), 2002.

Simulation-based Planning Simulation-based Control





Systems, 21(5), 2002.

Simulation-based Planning

Traditional simulation

(current status)



Some Decision-Making Problems

• Part releasing problem

• Part selection problem

• Machine selection problem

• Part buffering problem

• Robot location problem

• And, some other problems

H. Cho, Y. Son, A. Jones, Design and Conceptual Development of Shop Floor Controllers through the

Manipulation of Process Plans, International Journal of Computer Integrated Manufacturing, 19(4), 2006.



Part Selection Problem

Machine A Machine B Machine C

Cell-

dedicated

buffer

Robot

Part 1

Part 2

Part 4

o1

1

4

3 2

o2

o3

o7

o8

sa ja

To be processed

immediately

Part 3 o4

o5

sa

o6

ja

Part

A B

A

B A

B B

C





Machine Selection Problem

Machine A Machine B Machine C

Cell-

dedicated

buffer

Robot 3

1

4 1 2

Part

Part 1

Part 2

Part 4

o1 o2

o3

o7

o8

sa ja

To be processed

immediately

Part 3 o4

o5

so

o6

jo

A B

C

B A

B B

C





Integration of Simulation and Optimization

• Optimization via Simulation

• M. Jafferali, J. Venkateswaran, Y. Son, 2005, Performance Comparison of Search-based Simulation

Optimization Algorithms for Operations Scheduling, International Journal of Simulation and Process Modeling,

1 (1~2), 58~71.

• Simulation embedding optimization

• R. Kanetkar, Y. Son, C. Soto, 2006, Fuzzy Logic Based Reinforcement Learning Approach to Real-time Shop

Floor Decision-Making, Working Paper, The University of Arizona.

Performance

estimates Discrete-event Simulator

Candidate solution(s)

Optimization

Search Routines

Environment

E

Agent

A

Action

a

I

R

State

s i

r

Y f X





Systems, 21(5), 2002.

Simulation-based Planning

Traditional simulation

(current status)





Systems, 21(5), 2002.

Simulation-based Control

• Real-time clock

(parallelism)

• Task generation

• Message exchanges

with FSA




Formal model

Automatic Code Generation

for Controllers



Interactions among controllers

pick put open grasp .....

Door

opened

Shop Level Simulation



Interaction between Planning and Control





Automatic Models Generation

• Pre-MDA

• From RM

(ERP) or

other

standards

(e.g. CMSD

at NIST)

• Generation

of various

simulations

(analysis,

planning,

control)

Y. Son, R. Wysk, A. Jones, Simulation Based Shop Floor Control: Formal Model, Model Generation and

Control Interface, IIE Transactions on Design and Manufacturing, 35(1), 2003.



Hierarchical Process Plans (Cho et al, 2006)



Questions

• Simulation-based real-time planning and

control (shop floor)



Extension to Top Floor

• Analogies between shop floor and top floor in terms of system

components

– Physical entities and tasks

– Coordination model

– Simulation model

– Resource model

J. Venkateswaran and Y. Son, Hybrid System Dynamic – Discrete Event Simulation based

Architecture for Hierarchical Production Planning, International Journal of Production

Research, 43 (20), 2005.

S. Lee, Y. Son, and R. Wysk, Simulation Based Planning and Control: From Shop Floor to Top

Floor, Journal of Manufacturing Systems, 26 (2), 2007.



Physical Entities and Tasks

Series of tasks from

a part perspective

Series of tasks

from an order

(containing

multiple products)

perspective



Coordination Model (Top Floor)

J. Venkateswaran and Y. Son, Design and Development of a Prototype Distributed Simulation for

Evaluation of Supply Chains, International Journal of Industrial Engineering, 11(2), 2004.

Shop floor

Top floor



Simulation Models in Planning Stage (2)

(a) Shop floor simulation (b) Top floor simulation

(a) Shop floor simulation (b) Top floor simulation

Spawning (detailed)

Aggregated Same

Different



Some Important Issues

• Synchronization of times between member

simulations

• Standards for information being exchanged

– NIST CMSD (SISO standard)

– HLA/RTI (IEEE standard) => Web services



Implementation Infrastructure using WS

• Light-weight HLA/ RTI

using Web Services

technology

– W3C standard protocols

including XML, SOAP,

WSDL

– Platform independent

– Less than 20 methods

(initialize, advanceTime,

cons_advanceTime,

sendMessage,

getMessage, terminate,

and cleanup)

• Available in public

– Used for shop floor as

well as top floor



Demo

../../2008/Raytheon/run2.exe



(Emerging) Emergence Response Scenario

Plume simulation

(Dartmouth College) Metro simulation

(NIST)

Crowd simulation

(U of Arizona)

A. Shendarkar, K. Vasudevan, S. Lee, and Y. Son, Crowd Simulation for Emergency Response

using BDI Agents Based on Immersive Virtual Reality, Simulation Modeling Practice and

Theory, 16, 2008.

Goal: Training of policemen

in a game environment



(Emerging) Distributed Blood Network Model

Y. Son and H. Adra, Cyberinfrastructure for Coordinating Distributed Federations of

Simulations: A Distributed Blood Network Model, Collaborative proposal efforts

Collecting Locations

OptRTI Services,

Data & interactions

Processing Centers

OptRTI (Optimal Runtime Infrastructure)

Simulation Model

(SimCAD: Federate)

Adapter

(conservative/optimistic)

Transporter

Simulation Model

(SimCAD: Federate)

Adapter


Simulation Model

(Arena: Federate)

Adapter


Represented by Represented by Represented by

OptRTI Services,

Data & interactions

OptRTI Services,

Data & interactions

Simulation Model

(Arena: Federate)

Adapter


OptRTI Services,

Data & interactions

Hospital

Represented by

• Normal situation

• Emergency situation

../Korean_National_Emergency_Management_Agency/run2.exe



Questions

• Integration of multiple discrete event

simulations



Dynamic-Data-Driven Adaptive Multi-Scale

Simulation (DDDAMS) (NSF) • Simulation (fast-mode simulator, and task

generator) for a highly complex system

– Adaptive and localized abstraction during the simulation run

– Simulation steers the measurement process (affecting model

fidelity)

Traditional DDDAMS

N. Celik, S. Lee, K. Vasudevan, and Y. Son, DDDAS-based Multi-fidelity Simulation Framework for

Supply Chain Systems, IIE Transactions on Operations Engineering, 42(5), 2010.



Sensory data

Predetermined

fidelity level Assigned fidelity

level (δt)

Data filtering algorithm

Algorithm 1

Fidelity selection algorithm

Algorithm 2

Algorithm 4

Algorithm 3

Fidelity assigning algorithm

Real System

Machine 2 Machine 1 Machine 3 Machine n

. . .

Information request

Dynamic simulation model reconstruction

algorithm

Operations scheduling

(Task generation)

Information update

Filtered data/

Detected abnormality

Available computational

resource

DDDAS

Data flow

Control flow

Assigned fidelity level (δt)

Embedded Algorithms in DDDAMS Simulation



Data Avg of 100 Pts Moving Range

Input : a timestamp and data Return : a single filtered data value Stores past data points in the database Implemented in C++ (dll) and plugged into Arena Short training period required

Detects abnormal status of each sensor Applies X-bar and moving range control charts

Uses last 100 data points to calculate and Bounds: [-3, +3] Able to adapt to changing machine conditions

Algorithm 1: Data Filtering and Abnormality

Detection



Bayesian Belief Network (BBN)

TS1

TS12 RM12 CT12

RM1

TS22 RM22 CT22 TSn2 RMn2 CTn2 …

MT23 G23 V23 H23 … …

0.2 0.7

0.77

TS: Throughput status

RM: Raw material

CT: Cell temperature

Gi: Gas from the machine i

Vi: Vibration of the machine i

Normal state

Abnormal state

Observe

Implemented in MSBnx

S23

PTS12lTS1=

PMT23lTS22= 0.35

Algorithm 2: Simulation Model Fidelity

Selection using BBN

Cell-level

throughput Cell-level

inventory

Observed data based

on the current fidelity



min ( )

s.t.

i i i

i S

i

i S

C R RA

RA TR

Parameters S: Set of machines that require computational resource Ci: Penalty cost if the resource requirement for machine i is

not satisfied TR: Available resource Ri: Resource requirement for machine i Control Variables RAi: Resource assigned to machine i

It’s Knapsack Problem!

Greedy algorithm implemented with VBA in Arena

– Select the essential which has the biggest penalty cost Ci

– It gives a good solution within short running time

Algorithm 3: Simulation Model Fidelity

Assignment



4.1: Forecasting Mechanism

Predicting impact of execution of a task Implemented via fast mode simulation Implemented via meta-model (regression)

4.2: Actual Control Mechanism Dynamic task generation In this case, controlling the scheduling decision, i.e.,

• Part dispatching/routing • Job selection • Lot sizing • Change of production rules

Implemented via a Rule-based algorithm For a push system where production is lead by forecasting

Is a look-ahead algorithm in short Applies Fast Mode Simulation in synchronization with RTdddas Implemented in Arena Comprised of 2 fragments

Algorithm 4: Prediction and Task Generation



Questions

• Dynamic-data driven adaptive multi-scale

simulation (DDDAMS)



Hierarchical Supply Chain Planning

• Goal: Development of a robust production and distribution plan – Considering operational aspects for improved

feasibility

– Without taking too much computation time (as opposed to using DES to evaluate all the decisions)

– Robust against future disturbances

• Method: Use of SD+DES simulations and decision models

J. Venkateswaran and Y. Son, Hybrid System Dynamic -- Discrete Event Simulation based

Architecture for Hierarchical Production Planning, International Journal of Production

Research, 43 (20), 2005, 4397 - 4429.

J. Venkateswaran and Y. Son, Robust Supply Chain Planning using Multi-Resolution Hybrid

Models: Experimental Study, International Journal of Modeling and Simulation, 29(4), 2009.



SUPPLY CHAIN SCENARIO

Suppliers

Manufacturer

Retailers

Transportation

R1

Rr

Transportation

Information Flow

Communicative

Purchase Orders

Collaborative

(Vendor Managed Inventory)

Sale & Stock Data



SD-DES Interaction

• First stage: decision evaluation using SD model only

• Second stage: evaluation of a good decision using SD-DES

Demand

Forecast

Production

Order

Supplier

SD Model

Supplier DES Model

Supplier DES Control

Parameters

Supplier SD Control

Parameters

Shop Status

Production

Order

Manufacturer SD

Model

(Production Ordering)

Manufacturer DES Model

Manufacturer DES

Control Parameters

Manufacturer SD

Control Parameters

Shop

Status

Purchase Order

Component

Delivery Receipt

Transporter DES

Model

Transporter DES

Model

Manufacturer SD

Model

(VMI)

Retailer DES

Model

Retailers SD Control

Parameters

Dispatch

Order

Inventory &

Sales Data

Shipment

Order

Product

Delivery Receipt

Transport

Status



DEMO



Questions

• Hybrid system dynamic - discrete event

simulation architecture for hierarchical

production/distribution planning



Extended BDI for Various Applications

Error detection and resolution personnel

in complex mfg (Sponsor: AFOSR);

Evacuation behaviors under a terrorist

bomb attack (Sponsor: AFOSR, NIST)

Virtual stock investment

(Sponsor: AFOSR) Network of stake holders in an

enterprise (Sponsor: NSF)

Evacuation behaviors under fire in

a factory (Sponsor: UA)

BDI and

computing

framework

X. Zhao and Y. Son, 2008, BDI-based Human Decision-Making Model in Automated

Manufacturing Systems, International Journal of Modeling and Simulation, 28(3), 347-356.

A. Shendarkar, K. Vasudevan, S. Lee, Y. Son, 2008, Crowd Simulation for Emergency

Response using BDI Agents Based on Immersive Virtual Reality, Simulation Modelling

Practice and Theory,16, 1415-1429.

Y. Son, J. Jin, Extended BDI Framework and Technologies for Modeling Partial Human

Decision-Making, AFOSR Cognition & Decision Program Review Workshop, Fairborn, OH,

April, 2006.

S. Lee, N. Celik, Y. Son, 2009, An Integrated Simulation Modeling Framework for Decision

Aids in Enterprise Software Development Process, International Journal of Simulation and

Process Modeling, 5(1), 62-76.

K. Vasudevan and Y. Son, 2008, Concurrent Consideration of Evacuation Safety and

Productivity in Manufacturing Facility Planning using Multi-Paradigm Simulations,

Computers and Industrial Engineering (Submitted).



Safety and Productivity in Mfg Facility Planning

• Motivation 1: In manufacturing layouts

– Productivity has been a major concern

– Opportunities for safety concern under emergency

evacuation

• Motivation 2: In safety standards

– E.g. NFPA (National Fire Protection Authority) Life

Safety Code Handbook

– Static (regardless of details of layout) and used as

minimum requirements

• (General) Travel distance to an exit <= 400ft

• (In high hazard occupancies) Travel distance <= 75ft

• Width of passageway serving as an exit >= 44in



Research Goal

• Consider both productivity and evacuation

safety via

– (Traditional) Discrete event simulation: productivity

analysis

– (Novel) Synthetic human decision behavior model +

agent based simulation: evaluate evacuation safety

• Varying layout configurations

• Number of exits

• Exit capacities

• Arrangements of exits

• Width of corridors



Case: Auto. Drive Train (Engine + Transmission)

• 265 feet * 625 feet (=165,625 ft2)

• 70 ~ 220 people



Considered Layout Configurations for

Fabrication Area

Product Layout

Hybrid Layout

Process Layout

Group Technology Layout



Considered 70 Exit Configurations

• Each configuration has 4 exits (8 possible locations)

• Total of 70 configurations considered

1

2

3 4

8

5

6

7

8 4 70=£



Crowd Simulation Model Development Process

• More realistic crowd model

• Evacuation safety analysis



Extended Belief-Desire-Intention Framework

X. Zhao and Y. Son, 2008, BDI-based Human Decision-Making Model in Automated

Manufacturing Systems, International Journal of Modeling and Simulation, 28(3), 347-356.

Bratman, 1987

Rao and Georgeff, 1998

Zhao and Son, 2008



• CAVE (VR) at UA

• Construction of 3D

environment

– Google SketchUp

(OpenGL API)

– AutoMod Simulator (3D

Inventor

• Audio effects

– Virtual Sound Server

system

• VR wand

– input to system

• Goggle

Human-in-the-loop Experiment in CAVE

../../../Papers/Conferences/2008/FAIM2008/Presentation/cave_FacEvac_mostM.avi



Belief Module (Perceptual Processor)

• Bayesian belief network

– Mimic subjective evaluation of an environment

– Training stage: HIL experiment (varying environment)

• 13 subjects; IRB

– Operation stage: individual perception

Environment

Human

Perception



Real-time Planning Module (1) -- DFT

• Decision-Field-Theory

– Busemeyer and Townsend (1993)

– Evolution of preference of alternative options

• Proven to explain several psychological phenomena

– Extended DFT for dynamic environment (multi-

stage decision-making)

• Lee, Son, and Jin (2007) – Information Sciences

S. Lee, Y. Son, and J. Jin, 2008, Decision Field Theory Extensions for Behavior Modeling in

Dynamic Environment using Bayesian Belief Network, Information Sciences, 178(10), 2297-2314.

J. Busemeyer, J. Townsend, 1993, Decision Field Theory: A Dynamic-Cognitive Approach to

Decision Making in an Uncertain Environment, Psychological Review, 100, 432-459.



Real-time Planning Module (2) -- DFT

• P(t): Preference state

vector at time t

• S: Feedback matrix

• C: Comparison process

matrix that contrast the

weighted evaluations

• M: Personal evaluation

matrix of each option on

each attribute

• W(t): Attention weight

vector allocated to each

attribute at time t

prefe

ren

ce

Fixed Decision Time ND

time

Alternative A

Alternative B

n×1 (n×n)×(n×1) (n×n)×(n×m)×(m×1)

n options

m attributes



Preference evolving for the exit way instance

0

( ) 0 0

0

10.45 if ( )

0

0where Pr ( ( )) 0.43 if ( )

1

00.12 if ( )

0

W t

W t

W t W t

W t

1 1 1

2 2 2

( 1) ( ) ( )0.9 0.01 1 1 3.5 1.3

( 1) ( ) ( )0.01 0.9 1 1 1.3 3.5

p t p t w t

p t p t w t

Instance: Exit way (2)

n×1 (n×n)×(n×1) (n×n)×(n×m)×(m×1)

Dynamic Environment

BBN Inference



1. Get environmental information (Fire, Smoke,

Crowd, Distance to destination)

1. Get environmental information (Distance from knowledge)

2. Get M and W from BBN 3. Get preference from EDFT and calculate probability based on the multiple replications 4. Get path from Soar Pdown Pleft …

1

2

( ) 0.31Pr( )

( ) 0.69

pr tt

pr t

æ ö æ ö÷ ÷ç ç÷ ÷= =ç ç÷ ÷ç ç ÷÷ çç è øè ø

4 5 4 55 1

4 71 4 8 2

0 54

0 46

Risk Time

Risk

Time

. . PathM(t)

. . Path

.W(t)

.

æ ö÷ç ÷= ç ÷ç ÷çè ø

æ ö÷ç ÷= ç ÷ç ÷çè ø

2. Get M and W from BBN

3. Get preference from EDFT and calculate probability based on the multiple replications; Pr

is fed into Soar

4. Get path from Soar based on the random selection Path = Pdown

0.31

0.69

n= 1 2

Illustration of Planning with Permanent Worker (1)

Planning horizon n=3 3



Force Model for Velocity Avoiding Collision

• Calculation of velocity for each agent given 1) desired

destination and 2) force (Helbing et al., 2000)

0 0

( )

( ) ( ) ( )i i i ii i ij iW

j i Wi

d v t t tm m

dt

v e vf f f

exp / t

ij i ij ij i ij ij ij ij ij ji ijA r d B kg r d g r df n v t

Desired vs. current Interaction

with other

agent

Force

against wall

Social force:

psychological

Body force:

physical contact

Slide friction force:

physical contact

0, if 0( )

, otherwise

xg x

x

ij i jd r r ( )ij i jr r r

../../../Papers/Conferences/2008/FAIM2008/Presentation/AnyLogic_FacEvac.avi



Demo: Constructed Crowd Simulation

Factory

../../../Son_Bios_Photos_Interviews_Articles_Speeches/CBS/Screen_Movies_Sent/anylogic.avi

../../../Papers/Conferences/2008/FAIM2008/Presentation/AnyLogic_FacEvac.avi



Implementation Infrastructure using WS • Light-weight HLA/ RTI

using Web Services

technology

– W3C standard protocols

including XML, SOAP,

WSDL

– Platform independent

– Less than 20 methods

(initialize, advanceTime,

cons_advanceTime,

sendMessage,

getMessage, terminate,

and cleanup)

• Available in public

– Used for integrating

Anylogic, BBN (Netica

BBN), Soar



Simulation Results (1) – Best Exit

Configurations

• A more spread out exit configuration pattern

• More decision points (passage alternatives), but faster evacuation



Simulation Results (2) -- Observations

• No inverse relationship between safety and

productivity

better

better



Questions

• Human decision behavior model: emergency

evacuation



Automatic Models Generation (Son et al., 2003)

Y. Son, A. Jones, R. Wysk, 2003, Component Based Simulation Modeling from Neutral

Component Libraries, Computers & Industrial Engineering, 45(1), 141-165.



DB Instantiation (Son et al., 2003)



Automatic Models Generation (Son et al., 2003)

Generate Arena File

Generate ProModel

File



CMSD-based Mfg App. Interoperability

• SISO Standard: Core Manufacturing Simulation Data

Representation of “Machine” in CMSD, XML document, Lekin Scheduling Software, Arena

Simulation Software, and Wildcat Order/Inventory Software (all automatically generated)

CMSD

In XML

Schema

XML document

Lekin Scheduler Arena Simulation Software Wildcat Order/Inventory Software



Simulation of Network of Stake-Holders

ERP



Simulation-based Workforce Assignment in a

Social Network

• Goal: to develop an simulation framework to help

managers devise optimal workforce assignment

– short-term goal (productivity of projects)

– long-term goal (robustness)

• under

– Multi-organizational distributed software development

environment (Kuali)

– Considering the employees’ position values in the social network

SIE

Org.

Mgmt MIS



Proposed Workforce Assignment Framework

Considering Position and Equivalence Reputation (Rij)

Opinion

Formation

Innovation

Diffusion

Optimal Assignment Module using

Multi-Objective Optimization

Robustness Productivity

Optimal Team Assignment

of Projects

Proximity (Pij)Trustworhiness (Tij) Influence (Iij)

0.44

0.6

0.54

α(t) β(t) γ(t) δ(t)where α(t), β(t), γ(t), and δ(t) are

evaluated via DEP’s probability equations

E3 Algorithm

Position Value (Fij)

Regular

Equivalence (REij)

)()(.

)(

)(

)(

)(

)( htEVWtPA

t

t

t

t

htP

ijijijijij PtRtItTttF )()()()()(

Evaluation Module using

Agent-based Simulation Model

Structural

Equivalence (SEij)

N. Celik, H. Xi, D. Xu, Y. Son, R. Lemaire, K. Provan, 2010, Simulation-based Workforce

Assignment Considering Position in a Social Network, Simulation, in press.



Simulation Results

Instance: 3 projects assigned Instance: 30 projects assigned

• Dynamic change of regular equivalence levels in the agent based model

• Dynamic change of the preference state matrix P(t)T=[(t), (t), (t), δ(t)]



Summary (1)

• Distributed federation of simulation and optimization

models, hardware and software components

– Simulation-based planning and control (shop level)

– Top floor extension

• Supply chain simulation integrating member simulations

• DDDAMS: Adaptive, localized abstraction on the fly

• Hierarchical supply chain planning involving SD+DES

• Extension to other emerging applications

– Emergency response

– Network of stake-holders in a software enterprise

• Interoperability of manufacturing applications



Summary (2)

• (Education) SIE Department of The University

of Arizona

• (Research and Practice) Simulation

innovations

Processes

Management

System Employee

Engagement



Questions or Collaboration Opportunities

• Give me your card or send me an e-mail if you want to

receive our journal papers or discuss collaboration

• Dr. Young-Jun Son; [email protected]; 1-520-626-

9530

mailto:[email protected]

multi-paradigm simulation innovations for manufacturing ...with particular relevance to systems of...

Documents