Manufacturing Enterprise Systems

1

DMIIEngineering Decision Systems

• Manufacturing Enterprise Systems
• Engineering Design
• Operations Research
• Service Enterprise Engineering

2
DMII Division Academic Programs
GOALI Innovation Org. Change Don Senich
Dr. Warren DeVries
DMII
Manufacturing Process and Equipment
Engineering Decision Systems
Operations Research and Service Enterprise
Engineering Suvrajeet Sen
Manufacturing Machines and Equipment George A.
Hazelrigg
Engineering Design Delcie Durham
Manufacturing Enterprise Systems Abhijit
Deshmukh
Material Processing and Manufacture Jian Cao
Nano - manufacturing Julie Chen
3
Manufacturing EnterpriseSystems

• Abhijit Deshmukh
• Program Director

4
Manufacturing Enterprise Systems

• Typical proposal topics
• Scheduling of simple and complex systems
  (deterministic/uncertainty)
• GD T, simulation via hardware, manufacturing
  execution systems
• Facilities layout
• Simulation
• Statistical methods for diagnostics /control
• Supply chain management and optimization
• Benign manufacturing and technology for
  sustainable environment

5
Engineering Design Program

• Delcie R. Durham
• Program Director

6
NSFs Engineering Design Program NOW

• Fundamentals of the Product Realization System
• Design for X
• Design Theory
• Visualization and representation

7

Operations Research And Service Enterprise
Engineering
Suvrajeet Sen Program Director
8
Rest of the Talk

• OR and SEE
• Directions
• Multi-disciplinary Opportunities
• Mathematical Sciences Priority
• Sensors and Sensor Networks
• Human and Social Dynamics
• MUSES
• Multi-scale Modeling for Biological Systems
• Future
• Cyberinfrastructure
• DDDAS
• Health Care Delivery

9
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

10
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

11
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

12

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

13
Multi-disciplinary Opportunities

• Human and Social Dynamics
• Agents of change. Research will examine
  large-scale transformations, such as
  globalization and democratization the reciprocal
  relationship between individual and social
  action the evolution of society and its
  interaction with climate ... adaptation and
  resistance to technological change .
• Dynamics of human behavior Explorations into the
  dynamics of change in human behavior over time.
  Research on cognitive, linguistic, developmental,
  social, organizational, cultural, biological and
  other processes essential to understanding the
  dynamic phenomena of human behavior...
  engineering enhancements for studying human
  behavior.
• Decision making and risk Projects will explore
  how risk and the perception of risk are changing,
  and how these affect decision-making and help
  shape the dynamics of human and social behavior..
  .
• Spatial social science. spatial analytic
  techniques (GPS).
• Modeling human and social dynamics Modeling
  projects and projects that make significant use
  of empirical and theoretical models will aim to
  identify specific causal linkages and other
  behavioral relationships among the many potential
  confounding factors in explorations of human and
  social dynamics. .. stochastic agent-based
  modeling, complex social network analysis, and
  techniques for modeling human behavior and
  interaction using innovative information and
  engineering technologies

14

Multi-disciplinary Opportunities

• MUSES (Materials Use Science, Engineering,
  Society)
• the design of new materials and processes with
  environmentally benign impacts on biocomplex
  systems, as well as maximizing the efficient use
  of individual materials throughout their life
  cycles.
• Multidisciplinary teams, 400K/yr, 3-5yrs
• Small awards 100k over 1-2yrs

15

CyberInfrastructure Whole gt 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

16
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
17
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

18
DMII Vision of Cyberinfrastructure
19
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-Optimizatio
  n 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

20
CyberinfrastructureWorkshops 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)

21
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

22
Healthcare DeliveryAgenda Setting Efforts

• WTEC OR in Healthcare Delivery
• Engineering and the Health Care System NAE/IOM
  Project

23
From Problem Statement to Vision of a Transformed
Heath Care System

• IOM Reports

90,000 deaths/yr
55 receive recommended treatment
24
Questions