THE UNIVERSITY OF TEXAS AT EL PASO COLLEGE OF ENGINEERING
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Presented to Fourth General Assembly Cartagena Network of
Engineering CNE September 22nd, 2010, Metz, France Dr. Ricardo
Pineda [email protected][email protected] Chair Industrial,
Manufacturing & Systems Engineering Department
http://imse.utep.edu Director Research Institute for Manufacturing
& Engineering Systems http://rimes.utep.edu
http://rimes.utep.eduhttp://rimes.utep.eduhttp://rimes.utep.edu and
Engineering Systems Research Institute for Manufacturing
Understanding Engineered Complex Systems of Systems
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Agenda Who Are we? Why Complex System of Systems (CxSoS)?
Systems Systems of Systems CxSoS Research Challenges
Conclusions
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UTEP Roots 1 st degrees offered Engineer of Mines (1914) TSSMM
Engineer of Mines (1914) TSSMM B.S. in Mining Engineering (1919)
TCMM B.S. in Mining Engineering (1919) TCMM The only
Doctoral/Research Intensive University with a Mexican-American
majority student population College of Engineering
College at a Glance: 2008-2009 Enrollment: 2323 BS, 368 MS, 124
PhD Graduates: 258 BS, 114 MS, 16 PhD 80+ Faculty $17 M Research
Expenditures ($35 Research Revenues) $16.5 M total endowments
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Major Contributor to Diverse Workforce UTEP College of
Engineering is the: #1 producer of Hispanic American BS #1 producer
of Hispanic American BS #4 producer of Hispanic American MS #4
producer of Hispanic American MS #2 producer of Hispanic American
PhD #2 producer of Hispanic American PhD #1 Graduate School for
Hispanics (Hispanic Business Magazine)
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Current Research Centers Center for Transportation
Infrastructure Systems (CTIS) W.M. Keck Center for 3D Innovation
Research Institute for Manufacturing and Engineering Systems
(RIMES) Cyber ShARE Center for Excellence CSER Center for
Structural Integrity of Aerospace Systems
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RIMES Objectives To foster total systems-level thinking across
Colleges & Industry. To address applications oriented SoSE
research areas. To advance multi-disciplinary educational programs
(UG/G). To research/publish advances in SE emerging technologies
and practices. To stimulate the adoption of standards and best SE
practices within industries.
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Areas of Research MPT for Integrated Development SoS Formal
Requirements Methodologies MBSE & Modeling Languages (UML,
SysML) Application of SOA to SoS (Next Gen NCS) Trade-off studies
Lean/Enterprise SE SoS Reliability (Prognostics, Resilience) SoS
Risk Analysis
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Applied Research Health Care Delivery Systems: UMC/TT Energy
Systems: US Army, Raytheon, EPE Sustainability: USDA(2) Knowledge:
GDC KMS
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WHY ENGINEERED C X SOS?
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Challenges of the 21 st Century Engineering Challenges
According to NAE: Renewable Energy Renewable Energy Water
Conservation Water Conservation Environment Protection Environment
Protection Global Warming Global Warming Sustainability
Sustainability Improve medicine and healthcare delivery Improve
medicine and healthcare delivery Reducing vulnerability to human
and natural threats Reducing vulnerability to human and natural
threats Expand and enhance human capability and happiness Expand
and enhance human capability and happiness Grand Challenges and
Engineering Systems, Charles M. Vest, CESU, MIT, 2009
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What do the challenges have in common? Open Systems
Socio-Political implications Trans-disciplinary Science &
Engineering efforts Self Organization Knowledge Emergence Required
ECOSYSTEMS THEY ARE ALL ECOSYSTEMS
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SYSTEMS SOS C X SOS
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Systems Engineering Attributes Linear decomposition
Hierarchical Structure Design (logical, behavior, physical)
Architectural Synthesis Predetermined outputs and behavior
Emergence not allowed Environment compliance Attribute Behavior
Logical Physical Single Design Source: Systems Analysis, Design,
and Development, Charles S. Wasson, Wiley-Interscience 2006
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SE Complementary approach
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SoS Engineering n Stakeholders n Attributes n Capabilities n
Behaviors n Governances n N Interface Agreement SOS Interface
Agreement SOS Inter- Agent Outer- Agent Inner- Agent
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SOS Attributes Operational Independence of the Elements
Managerial Independence of the Elements Evolutionary Development
Incremental Knowledge Some Emergent Behavior Geographic
Distribution Source: Sheard Sarah, Third Millennium Systems,
LLC
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From Attributes to Functions Attribute 1 Att Capability n
Capability 1 Function n Function 1 Measure n Measure 1
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SOS Approach
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ENGINEERED SOS Legal Social Environment Ethical Semi-Closed
Privacy
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CxSOS Defined? Systems Science: Systems Science: It is an
interdisciplinary methodology to explain the emergence of certain
macroscopic phenomena via the non- linear interactions of
microscopic elements Systems Engineering View: Complex Systems are
systems that comprise many interacting parts with the ability to
generate a new quality of macroscopic collective behavior the
manifestations of which are the spontaneous formation of
distinctive temporal, spatial or functional structures [Springer
Complexity] Social Science: Social Science: The crucial point of
Complex Systems approach is that from a macroscopic point of view
the development of political, social, or cultural order is not only
the sum of single intentions, but the collective result of non-
linear interactions [REF]
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ENGINEERED CxSOS Emergence of Knowledge Self Organization
Privacy Legal Social Environment Ethical Autonomy OPEN
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CxSOS Attributes Attributes (NCSI, 2009)*** Environmental
Awareness Environmental Awareness Mutual Sustainment Mutual
Sustainment Autonomy Autonomy Self organizing Self organizing
Emergent macro-level behavior Emergent macro-level behavior Trust
Trust Attributes (Dagli/Ergin, 2009)* LTP impossible Unexpected
changes Patterns & ST predictability Evolutionary *Source:
Systems of Systems Architecting, C.H. Dagli, N. Kilicay-Ergin, in
Systems of Systems Engineering, Wiley Series in Systems
Engineering. 2009 Attributes/(Sheard, 2009)** Non-linear behavior
(initial condition) Non-linear behavior (initial condition)
Non-predictable Non-predictable May evolve from order to chaos May
evolve from order to chaos Self-organized Self-organized Whole may
be greater than sum of the parts Whole may be greater than sum of
the parts Emergent Adaptive behavior Emergent Adaptive behavior
Ecosystem more fit as it becomes more connected Ecosystem more fit
as it becomes more connected **Source: Principles of Complex
Systems for Systems Engineering, S. Sheard, A. Mostashari, Systems
Engineering Vol. 12, No 4, 2009 ***Source: Net-Centric Services
Framework, V2.1, Net-Centric Operations Industry Consortium,
2009
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Process Interface Adaptive Knowledge & Autonomy?? Attribute
Criteria Knowledge Domain Interface Hardware Layer Software Layer
Process Service Application Governance
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Importance of End-to-End Systems Thinking Challenges more and
more trans-disciplinary Enterprise-centric to end-user centric To
design for optimizing desired objectives To predict design
attributes of the Systems that are not inherent in the blocks (The
whole is greater than the sum of its parts) To design for dynamic
Stakeholder Requirements changes (flexibility, modularity,
re-usability, etc.)
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Educational Challenges Charles M. Vest has observed Charles M.
Vest has observed the engineering world is disappearing. Making
universities and engineering schools exciting, creative,
adventurous, rigorous, demanding, and empowering environments is
more important than specifying curricular details. From Casual
Newtonian decomposition Analysis to Non- linear analysis
Trans-disciplinary and experiential learning: team environment
(increased organization/ people connectivity)
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Research CHALLENGES What is the interconnection structure to
optimize the objective of CxSOS? How does organizational
connectivity affect knowledge emergence and what are the processes
to maximize them? Are there any Adaptation laws to evolve functions
of the overall CxSOS? Is Knowledge Emergence dictated or limited by
Interface agreement? Can new methods avoid unpredictable emergent
behavior? V&V Methodologies T & E Procedures/
Technologies
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Research LMC-Aeronautics Skunk works (LMC R&D) Jacobs
Technology. Raytheon Research IDS, Energy Hamilton Sundstrand TT-
UMC
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Conclusions Organizational challenges may be more limiting to
Knowledge emergence than advances in technology (adaptive behavior
more prevalent than emergent knowledge) There is no consensus among
SE community on Engineered SOS/CxSOS attributes. Need to agree on
complexity metrics to reduce design complexity. Design Optimization
to the Complexity metrics. MPT to adapt to constant stakeholders
requirements changes a MUST. Need of NEW IV&V MPT an urgent
need/demand Application of Science principles to design engineering
in its infancy