research team

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RESEARCH TEAM INVESTIGATORS G. Berkstresser S. Fang R. King T. Little H. Nuttle J. Wilson Textiles and Apparel Mgmt. Industrial Engineering Industrial Engineering Textiles and Apparel Mgmt. Industrial Engineering Industrial Engineering STUDENTS H. Cheng S. Lertworasirikul Y. Liao S. Wang Ph.D. Operations Research Ph.D. Industrial Engineering Ph.D. Industrial Engineering Ph.D. Operations Research

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RESEARCH TEAM. INVESTIGATORS G. Berkstresser S. Fang R. King T. Little H. Nuttle J. Wilson. Textiles and Apparel Mgmt. Industrial Engineering Industrial Engineering Textiles and Apparel Mgmt. Industrial Engineering Industrial Engineering. STUDENTS H. Cheng S. Lertworasirikul - PowerPoint PPT Presentation

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Page 1: RESEARCH TEAM

RESEARCH TEAM

INVESTIGATORSG. BerkstresserS. FangR. KingT. LittleH. NuttleJ. Wilson

Textiles and Apparel Mgmt.Industrial EngineeringIndustrial EngineeringTextiles and Apparel Mgmt.Industrial EngineeringIndustrial Engineering

STUDENTSH. ChengS. LertworasirikulY. LiaoS. Wang

Ph.D. Operations ResearchPh.D. Industrial EngineeringPh.D. Industrial EngineeringPh.D. Operations Research

Page 2: RESEARCH TEAM

SUPPLY CHAIN

How many to produce?

Where to build inventory?

Where to source materials?

Subcontractor

Manufacturer

Manufacturer

Manufacturer

DC

DC

Customers

Retailer

Retailer

Retailer

Retailer

Supplier

Supplier

Should we subcontract?

How many distribution centers?

How many to produce?How many to produce?

Where to build inventory?

Where to build inventory?

Where to source materials?

Where to source materials?

SubcontractorSubcontractor

Manufacturer

Manufacturer

Manufacturer

DC

DC

Customers

Retailer

Retailer

Retailer

Retailer

Supplier

Supplier

Should we subcontract?

How many distribution centers?

How many distribution centers?

Page 3: RESEARCH TEAM

OBJECTIVES

Develop Decision Support Tools for Integrated Supply Chain Design and Management

Incorporate vagueness and uncertainty through the use of Fuzzy Mathematics.

Demonstrate prototypes.

Page 4: RESEARCH TEAM

SUPPLY CHAIN MODELING AND OPTIMIZATIONUSING SIMULATION & SOFT COMPUTING

Supply chains involve the activity and interaction of many entities.

Decision makers typically have imprecise goals. e.g. “Low work – in – process”

Some system parameters may also be imprecise. e.g. “Production rate”

Discrete event simulation can help design and analyze supply chains.

Many configurations and courses of action need to be investigated.

Even experts have to spend a considerable amount of time searching for good alternatives.

Soft computing guided simulation speeds up the process.

Page 5: RESEARCH TEAM

ITERATIVE PROCESS SCHEME

Supply Chain Configuration

Simulation

Activate Fuzzy Rules/Logic

Goals met?

Stop

Input - PerformanceData

Fuzzy System / Relationship

Identification

KnowledgeExtraction

Soft ComputingGuided Simulation

Yes

No

Page 6: RESEARCH TEAM

RULE BASE GUIDE TO SUPPLY CHAIN RECONFIGURATION

Rule example 1:If Overall work-in-process is High

then Change in production rate in the Cutting facility is

Positively Small.

Rule example 2:If Overall work-in-process is High

and Utilization at the cutting facility is High

then Change in production rate in the Cutting facility is

Positively Large.

Page 7: RESEARCH TEAM

RESULTS

Satisfactory results (high service level) achieved in few iterations.

Mem

bers

hip

Iteration

Page 8: RESEARCH TEAM

SUPPLY CHAIN INTEGRATOR

Analyse and Compare Designs Analyse and Compare Designs and Operational Practicesand Operational Practices

Subcontractor

Manufacturer

Manufacturer

Manufacturer

DC

DC

Customers

Retailer

Retailer

Retailer

Retailer

Supplier

Supplier

Page 9: RESEARCH TEAM

STEPS

ConfigurationConfiguration

Create your own supply chain using the drag&drop featureCreate your own supply chain using the drag&drop feature

Set/Adjust ParametersSet/Adjust Parameters

Specify/adjust parameters using dialog boxesSpecify/adjust parameters using dialog boxes

SimulationSimulation

Simulate the integrated operation of the supply chainSimulate the integrated operation of the supply chain

ReportingReporting

Obtain detailed performance measure reportObtain detailed performance measure report

Page 10: RESEARCH TEAM

SUPPLY CHAIN CONFIGURATION

Page 11: RESEARCH TEAM

PARAMETER SETTING

Page 12: RESEARCH TEAM

DUE-DATE NEGOTIATOR

Version 1 - bargaining with monetary Version 1 - bargaining with monetary penalty and compensation penalty and compensation

Version 2 - explore resource expansion Version 2 - explore resource expansion alternativesalternatives

Version 3 – real time order entryVersion 3 – real time order entry

• A tool for order delivery date negotiation A tool for order delivery date negotiation between a manufacturer and customers.between a manufacturer and customers.

• Methodology: Genetic Algorithms, Fuzzy Methodology: Genetic Algorithms, Fuzzy Modeling, Fuzzy LogicModeling, Fuzzy Logic

Page 13: RESEARCH TEAM

DUE-DATE NEGOTIATOR

Assignment / BargainerAssignment / Bargainer

Page 14: RESEARCH TEAM

DUE-DATE NEGOTIATOR

Resource UtilizationResource Utilization

Page 15: RESEARCH TEAM

DUE-DATE NEGOTIATOR

Assignment / SchedulerAssignment / Scheduler