DMII Engineering Decision Systems
DMII Engineering Decision Systems •Manufacturing Enterprise Systems •Engineering Design •Operations Research •Service Enterprise Engineering
DMII Division Academic Programs GOALI Innovation & Org. Change
Dr. Warren DeVries
Don Senich
DMII DMII Manufacturing Process and Equipment
Engineering Decision Systems
Operations Research and Service Enterprise Engineering Suvrajeet Sen
Manufacturing ManufacturingEnterprise Enterprise Systems Systems Abhijit AbhijitDeshmukh Deshmukh
Engineering Design Delcie Durham
Material Processing and Manufacture Jian Cao
Manufacturing Machines and Equipment George A. Hazelrigg
Nano - manufacturing Julie Chen
OR and SEE Programs • Each has a base budget of ~$3-3.5M/year • Funds both CAREER (Junior Faculty) and Unsolicited • Same deadlines as other DMII programs • Investments in 2001-2003 account for 30% of the budget in 2005 • Plan to maintain CAREER investments stable
Now, the Good News • Important to work on Multi-disciplinary Research • Multi-disciplinary Opportunities – – – – –
Mathematical Sciences Priority Sensors and Sensor Networks Human and Social Dynamics MUSES Multi-scale Modeling for Biological Systems
Multi-disciplinary Opportunities • Mathematical Sciences – Large Data Sets • e.g. Inference, Learning and real-time dynamic optimization
– Uncertainty • e.g. Decision-making in stochastic environments
– Complex systems • e.g. Modeling, control and optimization of multi-scale systems
– Submission to either Division of Math. Sciences or any participating Engineering Division
Sensor and Sensor Networks • 3 topical areas – – Design, materials, and concepts for new sensors and sensing systems – Sensor arrays – Interpretation and use of sensor data
• 3 award sizes – Investigators from same discipline $125k/yr, 3yrs – Small interdisciplinary team $250/yr, 3yrs – Large interdisciplinary groups $500k/yr, 5yrs
CyberInfrastructure: Whole > Sum of the Parts •CI Background •CI and Engineering: Why? •Some Views on CI •Layering for Engineering •Operations Research and Service Enterprise Eng •DMII Views
What is Cyberinfrastructure? • “Atkins report”
– Blue-ribbon panel, chaired by Daniel E. Atkins (Michigan)
• A national, reliable and dynamic, interoperable and integrated system of hardware, software, and data resources and services. • This new infrastructure would open the door to new types of scientific/engineering research and education. http://www.cise.nsf.gov/sci/reports/atkins.pdf
The CI Vision • Cyberinfrastructure (CI): a data, models, software, and physical infrastructure for research and education • Promote greater collaboration via shared resources (data, models, software, physical assets) • Why greater collaboration? – Improved resources for multi-disciplinary research – Engineering, a multi-disciplinary pursuit, stands to be a major beneficiary
DMII Vision of Cyberinfrastructure
CYBERPLATFORMS Grid-computing Interconnection-design, operating systems, faulttolerance, security, inter-operability, sensor networks
CYBERTOOLS Bridging domain needs with the cyber platform tools Grid-enabled modeling tools, Benchmarking tools, Implementation support, Interoperability between models/algorithms etc.
CYBERDOMAINS Domain-specific models and tools (USERS) Applications for critical service infrastructure, design, analysis
OR/SEE Perspective •
Domain-Specific Layer (CyberDomains) – Health care applications – Risk in critical service infrastructure (e.g. Electric Power) – Other applications of national need (e.g. security)
•
Domain-Neutral Layer (Cybertools) – – – – – –
•
Probabilistic Models (Markov Decision Models, Queuing Networks) Optimization (Linear, Integer, Graph, Combinatorial etc.) Simulation (Discrete-Event, Simulation-Optimization etc.) Integrative Models (Stochastic Optimization) Languages and Testbeds (Optimization, Simulation etc.) Interoperability of optimization and simulation
Platform Layer for Software Exchange (CyberPlatform) – Interoperability of software – Performance of online exchange
Cyberinfrastructure Workshops in ENG • • • • •
June 2003 (ENG Directorate) January 6, 2004 (DMII Division) January 13,14 (ENG Directorate) April 21,22 (ENG Directorate) August 30, 31(DMII Division)
Dynamic Data Driven Application Systems • New data is infused into a model • E.g. Continual optimization • Compelling application is a must (e.g. real-time decisions in advanced manufacturing • Solicitation will be out in the near future
Healthcare Delivery
Agenda Setting Efforts WTEC OR in Healthcare Delivery Engineering and the Health Care System: NAE/IOM Project
From Problem Statement to Vision of a Transformed Heath Care System IOM Reports:
90,000 deaths/yr
55% receive recommended treatment
Questions
DMII Division Academic Programs GOALI Innovation & Org. Change
Dr. Warren DeVries
Don Senich
DMII DMII Manufacturing Process and Equipment
Engineering Decision Systems
Operations Research and Service Enterprise Engineering Suvrajeet Sen
Manufacturing ManufacturingEnterprise Enterprise Systems Systems Abhijit AbhijitDeshmukh Deshmukh
Engineering Design Delcie Durham
Material Processing and Manufacture Jian Cao
Manufacturing Machines and Equipment George A. Hazelrigg
Nano - manufacturing Julie Chen
OR and SEE Programs • Each has a base budget of ~$3-3.5M/year • Funds both CAREER (Junior Faculty) and Unsolicited • Same deadlines as other DMII programs • Investments in 2001-2003 account for 30% of the budget in 2005 • Plan to maintain CAREER investments stable
Now, the Good News • Important to work on Multi-disciplinary Research • Multi-disciplinary Opportunities – – – – –
Mathematical Sciences Priority Sensors and Sensor Networks Human and Social Dynamics MUSES Multi-scale Modeling for Biological Systems
Multi-disciplinary Opportunities • Mathematical Sciences – Large Data Sets • e.g. Inference, Learning and real-time dynamic optimization
– Uncertainty • e.g. Decision-making in stochastic environments
– Complex systems • e.g. Modeling, control and optimization of multi-scale systems
– Submission to either Division of Math. Sciences or any participating Engineering Division
Sensor and Sensor Networks • 3 topical areas – – Design, materials, and concepts for new sensors and sensing systems – Sensor arrays – Interpretation and use of sensor data
• 3 award sizes – Investigators from same discipline $125k/yr, 3yrs – Small interdisciplinary team $250/yr, 3yrs – Large interdisciplinary groups $500k/yr, 5yrs
CyberInfrastructure: Whole > Sum of the Parts •CI Background •CI and Engineering: Why? •Some Views on CI •Layering for Engineering •Operations Research and Service Enterprise Eng •DMII Views
What is Cyberinfrastructure? • “Atkins report”
– Blue-ribbon panel, chaired by Daniel E. Atkins (Michigan)
• A national, reliable and dynamic, interoperable and integrated system of hardware, software, and data resources and services. • This new infrastructure would open the door to new types of scientific/engineering research and education. http://www.cise.nsf.gov/sci/reports/atkins.pdf
The CI Vision • Cyberinfrastructure (CI): a data, models, software, and physical infrastructure for research and education • Promote greater collaboration via shared resources (data, models, software, physical assets) • Why greater collaboration? – Improved resources for multi-disciplinary research – Engineering, a multi-disciplinary pursuit, stands to be a major beneficiary
DMII Vision of Cyberinfrastructure
CYBERPLATFORMS Grid-computing Interconnection-design, operating systems, faulttolerance, security, inter-operability, sensor networks
CYBERTOOLS Bridging domain needs with the cyber platform tools Grid-enabled modeling tools, Benchmarking tools, Implementation support, Interoperability between models/algorithms etc.
CYBERDOMAINS Domain-specific models and tools (USERS) Applications for critical service infrastructure, design, analysis
OR/SEE Perspective •
Domain-Specific Layer (CyberDomains) – Health care applications – Risk in critical service infrastructure (e.g. Electric Power) – Other applications of national need (e.g. security)
•
Domain-Neutral Layer (Cybertools) – – – – – –
•
Probabilistic Models (Markov Decision Models, Queuing Networks) Optimization (Linear, Integer, Graph, Combinatorial etc.) Simulation (Discrete-Event, Simulation-Optimization etc.) Integrative Models (Stochastic Optimization) Languages and Testbeds (Optimization, Simulation etc.) Interoperability of optimization and simulation
Platform Layer for Software Exchange (CyberPlatform) – Interoperability of software – Performance of online exchange
Cyberinfrastructure Workshops in ENG • • • • •
June 2003 (ENG Directorate) January 6, 2004 (DMII Division) January 13,14 (ENG Directorate) April 21,22 (ENG Directorate) August 30, 31(DMII Division)
Dynamic Data Driven Application Systems • New data is infused into a model • E.g. Continual optimization • Compelling application is a must (e.g. real-time decisions in advanced manufacturing • Solicitation will be out in the near future
Healthcare Delivery
Agenda Setting Efforts WTEC OR in Healthcare Delivery Engineering and the Health Care System: NAE/IOM Project
From Problem Statement to Vision of a Transformed Heath Care System IOM Reports:
90,000 deaths/yr
55% receive recommended treatment
Questions
