Systems Engineering ResearchTaking Systems Engineering to the Next Level
Cihan H Dagli, PhDProfessor of Engineering Management and Systems Engineering
Professor of Electrical and Computer Engineering Founder and Boeing Coordinator of Systems Engineering Graduate Program
INCOSE and IIE Fellow
MISSOURI UNIVERSITY OF SCIENCE AND TECHNOLOGYRolla, Missouri, U.S.A.
Outline
• Introduction– Need for Systems Architecting and Engineering– DoD Systems Engineering Vision 2020
• Academia Needs• Missouri S&T’s Approach
– Smart Systems Architecting– Courses– Industry Cooperation
• Future Of Systems Architecting
Research Collaborators
• Renzhong Wang (Current SysEng PhD Student, INCOSE Doctoral Award Recipient)
• Dr. Atmika Singh (Former SysEng PhD Student, Researcher at Clearway Holding)
• Dr. Jason Dauby (Former SysEng PhD Student, INCOSE Doctoral Award Recipient, Researcher at Naval Surface Warfare Center)
• Dr. Nil Kilicay Ergin (Former SysEng PhD Student, Faculty at Pen State University)
Introduction
The Dynamically Changing Operating Environment – We are increasingly a networked society:
• Trans-national mega military systems• Asymmetrical threats vs. rapid reaction forces• Trans-national enterprises • Trans-national manufacturing • Globally distributed services and production
– We are increasingly dependent on these networks.
Decision Analysis Provides More Customer Interaction and a Better Product
Decision Analysis Provides More Customer Interaction and a Better Product
Wants
Needs Desires
Wishes
Must Haves
Decision Analysis techniques are toolsused to solve complex problems
through a structured process
MultipleMultipleParticipantsParticipants
ConflictingConflictingInterestsInterests
MultipleMultipleObjectivesObjectives
• Consensus• Common Terminology• List of Potential Trades
• Ranked Priorities• Documentation
CompetingCompetingAlternativesAlternatives
Multiple Multiple DisciplinesDisciplines
Decision Analysis Provides More Customer Interaction and a Better Product
Decision Analysis Provides More Customer Interaction and a Better Product
Wants
Needs Desires
Wishes
Must Haves
Decision Analysis techniques are toolsused to solve complex problems
through a structured process
Decision Analysis techniques are toolsused to solve complex problems
through a structured process
MultipleMultipleParticipantsParticipants
ConflictingConflictingInterestsInterests
MultipleMultipleObjectivesObjectives
• Consensus• Common Terminology• List of Potential Trades
• Ranked Priorities• Documentation
CompetingCompetingAlternativesAlternatives
Multiple Multiple DisciplinesDisciplines
• Prognostics & Health Management
• Opportunistic maintenance
• Interactive Tech Manuals
• Prognostics & Health Management
• Opportunistic maintenance
• Interactive Tech Manuals
• Flexible support• Flexible support
• Proactive manufacture
• Proactive supply
• Autonomic distribution
• Spares usage & trends
• Projected spares needs
POLPipelines
CODResupply
GroundDeliveries
Repair&
Overhaul
AircraftVehicle
EquipmentGeneration &Maintenance
IntermediateRepairOEM
• 24/7 Response centers
• Digital Engineering Links
• 24/7 Response centers
• Digital Engineering Links
NetworkNetwork--Centric FutureCentric Future
Effectiveness
• Survivability• Vulnerability• Mission Success
Advanced Supportability
• Supply Chain Mgt.• Maintenance Mgt.
Analysis• Supply Mgt. Analysis
• Operational C4ISR• Communications• Dynamic Systems• System of Systems
• Visualize Scenarios• Immerse Man in Loop
Decision Analysis
• Voice of Customer• Customer Requirements• Expert Judgment
• LCC/TOC• Design to Cost• Best Value
PointEstimates
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2000
1Q 2Q 3Q 4 Q
2001
1Q 2 Q 3Q 4Q
2002
1Q 2 Q 3Q 4Q
2003
1Q 2 Q 3Q 4 Q
2004
1Q 2Q 3Q 4 Q
2005
1Q 2Q 3Q 4 Q
Ph as e I I Sta r t EM D St ar t
F lt De mo 1 F lt De m o 2 F lt D em o 3 F lt D em o 4
R R&OE St ar t
L ast revisi on:
F lt De mo 5
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UCAV d ec ision aid s flig ht dem o
D elive r B - 2 w eap ond elive ry GWI S
JSF /UCAV Com m ona lity S t udy
Es tab lish Com mo n Av ion icsD eve lopm e nt G ro up
D EMPC UD S Fo rm at ion Ta xi/F ligh t ( fixe d g eom , pos sep a lgor ithm s)
UDS Coo rd inat ed mo tion , va ria bleg eom e trie s / d ec onf liction alg or ithm s
Glo bal the ate r m ult i-le vel n etw or king de mo
BOL DSTR OKE d em os
S ing le s imu late dve hicle dis trib ute dco ntr ol la b d em o
Re al-t ime so ftwa re ar chite ctu re& d esig n d em o
UCAV d ecis ion aids lab de mo(c ont inge ncy m ana ge me nt)
R eal- tim e d istr ibu ted pr oce ssin g
AJ/L P I LO S C2 Dem o
Sof twar e reu se me tr ics tr ac king
Dem o of O M P & Int ellig entMa inte na nce A ids /PMT & I MSS
L ab & flig ht d em o - O MP/ miss ion /veh icles yste ms inte gr atio n
AT3 o r PL AID te st o n U CAV
So ftwa re re use m etr ics t ra cking
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M ult i-se nso r m ult i-so ur ce dat a fu sio n
AJ/L P I BLO S C2 -AJ G PS Dem o
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Decis ion aids forope ra tor ha nd offlab dem o
Aut om ate d d yna m ic m issio nr epla nn ing fligh t te st d em o
Dro p m ult iple pr e- plan ne d sm a ll
bo mb s fr om M BR with fu ll SM S
U DAS A lgo rith mic Con tr ol F ligh t T est Dem o( M ulti- Vehic le Co or din ate d F ligh t, C ollisio nAv oida nce , Se nso r P la nn er , Aut oro ute r)
L os s of com m co ntin ge ncy fligh t d em oSu pplie r soft war e p ro duc tivity de mo
PH M/O M P
Fa llb ac k:
Su p p lier s
B oe in g
U CAV AT D RR &OE
G o v’t S&T
U CAV AT D Ph as e I I
U CAV AT D Ph as e I
} UT P
Pri ma ry: F ully inte gr ate ds oftw are fu nct iona lity.
De cre ase d s oft war ef unc tion ality .
Unmanned C ombat Air V ehicleAdvanced Technology Demonstration
UCAV - A TD
Phase II - Affordabi lity / LCC P lan
Pr e pared by:
David M cC aughey (B oeing )
C oncurred by:
Ku rt Bau sch (B oeing)
S teve Ras t (S AI C)
App roved b y:
Ph il P anag os (Boeing)
Lt Col Mic hae l Leah y (U SA F)
AffordabilityPlan
$ $
HistoricalRegression
SupplierOptions
Un
it C
os
t
JSF
UCAV
1/3 the cost of JSFO
&S
Co
st F-16
UCAV
75% Reduction from F-16
Time
$
CostTargets
Time
$
Time
$
Development /Investment Plans
Time
$
Focus:• System Cost
Drivers
• Figures of Merit
• Effectiveness & Affordability Balance
• Investment Planning
CostUncertaintySimulation
LCC Probability
0%
20%
40%
60%
80%
100%
EMDProdO&S
$$
Kurt Bausch314-232-6917
PointEstimates
Ris
k F
act
or
Lo
wM
edH
igh
UCA V AT DPha se I
UCAV ATDPh ase II
RR &OEEM D
1998
1 Q 2 Q 3Q 4Q
1999
1Q 2 Q 3Q 4Q
2000
1Q 2Q 3Q 4 Q
2001
1Q 2 Q 3Q 4Q
2002
1Q 2 Q 3Q 4Q
2003
1Q 2 Q 3Q 4 Q
2004
1Q 2Q 3Q 4 Q
2005
1Q 2Q 3Q 4 Q
Ph as e I I Sta r t EM D St ar t
F lt De mo 1 F lt De m o 2 F lt D em o 3 F lt D em o 4
R R&OE St ar t
L ast revisi on:
F lt De mo 5
Pha se II En d
UCAV d ec ision aid s flig ht dem o
D elive r B - 2 w eap ond elive ry GWI S
JSF /UCAV Com m ona lity S t udy
Es tab lish Com mo n Av ion icsD eve lopm e nt G ro up
D EMPC UD S Fo rm at ion Ta xi/F ligh t ( fixe d g eom , pos sep a lgor ithm s)
UDS Coo rd inat ed mo tion , va ria bleg eom e trie s / d ec onf liction alg or ithm s
Glo bal the ate r m ult i-le vel n etw or king de mo
BOL DSTR OKE d em os
S ing le s imu late dve hicle dis trib ute dco ntr ol la b d em o
Re al-t ime so ftwa re ar chite ctu re& d esig n d em o
UCAV d ecis ion aids lab de mo(c ont inge ncy m ana ge me nt)
R eal- tim e d istr ibu ted pr oce ssin g
AJ/L P I LO S C2 Dem o
Sof twar e reu se me tr ics tr ac king
Dem o of O M P & Int ellig entMa inte na nce A ids /PMT & I MSS
L ab & flig ht d em o - O MP/ miss ion /veh icles yste ms inte gr atio n
AT3 o r PL AID te st o n U CAV
So ftwa re re use m etr ics t ra cking
SAR f ligh t te sto n UC AV
M ult i-se nso r m ult i-so ur ce dat a fu sio n
AJ/L P I BLO S C2 -AJ G PS Dem o
A ir tra ffic mg t d em o
Decis ion aids forope ra tor ha nd offlab dem o
Aut om ate d d yna m ic m issio nr epla nn ing fligh t te st d em o
Dro p m ult iple pr e- plan ne d sm a ll
bo mb s fr om M BR with fu ll SM S
U DAS A lgo rith mic Con tr ol F ligh t T est Dem o( M ulti- Vehic le Co or din ate d F ligh t, C ollisio nAv oida nce , Se nso r P la nn er , Aut oro ute r)
L os s of com m co ntin ge ncy fligh t d em oSu pplie r soft war e p ro duc tivity de mo
PH M/O M P
Fa llb ac k:
Su p p lier s
B oe in g
U CAV AT D RR &OE
G o v’t S&T
U CAV AT D Ph as e I I
U CAV AT D Ph as e I
} UT P
Pri ma ry: F ully inte gr ate ds oftw are fu nct iona lity.
De cre ase d s oft war ef unc tion ality .
Unmanned C ombat Air V ehicleAdvanced Technology Demonstration
UCAV - A TD
Phase II - Affordabi lity / LCC P lan
Pr e pared by:
David M cC aughey (B oeing )
C oncurred by:
Ku rt Bau sch (B oeing)
S teve Ras t (S AI C)
App roved b y:
Ph il P anag os (Boeing)
Lt Col Mic hae l Leah y (U SA F)
AffordabilityPlan
$ $
HistoricalRegression
SupplierOptions
Un
it C
os
t
JSF
UCAV
1/3 the cost of JSFO
&S
Co
st F-16
UCAV
75% Reduction from F-16
Time
$
CostTargets
Time
$
Time
$
Development /Investment Plans
Time
$
Focus:• System Cost
Drivers
• Figures of Merit
• Effectiveness & Affordability Balance
• Investment Planning
CostUncertaintySimulation
LCC Probability
0%
20%
40%
60%
80%
100%
EMDProdO&S
$$
Kurt Bausch314-232-6917
Introduction
Courtesy of Dr. Mike McCoy
Introduction
(Adopted from An Overview of Global Earth Observation System of Systems (GEOSS), Stefan Falke, Geospatial Intelligence Operating Unit, Northrop Grumman Corporation)
Introduction
Super-Efficient , Eco-Friendly, and People Friendly
Trans-national Manufacturing
Need for Systems Architecting and Engineering• Systems Engineering: An interdisciplinary approach and
means to enable the realization of successful systems. Systems Engineering considers both the business and the technical needs of all stakeholders with the goal of providing a quality product that meets the user needs.
• System Architecture: The aggregation of decomposed system functions into interacting system elements whose requirements include those associated with the aggregated system functions and their interfaces requirements/definition
INCOSE (International Council of Systems Engineers)
*Source: INCOSE Systems Engineering Center of Excellence SECOE 01-03 INCOSE 2003; & Honour, E. “Understanding Value of Systems Engineering”, INCOSE Conference, June 20-24, 2004
Cost and Schedule Performance as a Function of Systems Engineering Effort
Need for Systems Architecting and Engineering
Need for Systems Architecting and Engineering
• Performed by NDIA in conjunction with the SEI in 2006-2008
• Surveyed 64 projects at defense contractors to assess:
• Data was collected anonymously to encourage honest and accurate reporting.
• Results published at:• http://www.sei.cmu.edu/publications/d
ocuments/08.reports/08sr034.html
*Source: Presentation of Joe Elm from Software Engineering Institute Carnegie Mellon University
Need for Systems Architecting and Engineering
*Source: Presentation of Joe Elm from Software Engineering Institute Carnegie Mellon University
39%
46%
15%
29%
59%
12%
31%
13%
56%
BestPerformance( x > 3.0 )
ModeratePerformance( 2.5 x 3.0 )
Lower Performance( x < 2.5 )
Lower Capability
( x 2.5 )N = 13
Moderate Capability
( 2.5 < x < 3.0 )N = 17
HigherCapability
(x 3.0 )N = 16
Gamma = 0.32p = 0.04
1.00
0.75
0.50
0.25
0.00
PROJECT PERFORMANCE vs. TOTAL SE CAPABILITY
Need for Systems Architecting and Engineering
*Source: Presentation of Joe Elm from Software Engineering Institute Carnegie Mellon University
Need for Systems Architecting and Engineering
*Source: Presentation of Joe Elm from Software Engineering Institute Carnegie Mellon University
Need for Systems Architecting and Engineering
• Architectures are fundamental to the success of the program • Architecture selection is a search process based on
ambiguous information and data• Architecture selection requires assessment methods based
on ambiguous key performance parameters to identify compromise architecture
• Architecting process is reduction of ambiguity hierarchically
DoD Systems Engineering Vision 2020
• Design Principles– Platform Based Engineering
Using a common core platform to develop many related systems/capabilities
– Trusted System DesignDeveloping trusted systems from untrusted components
DoD Systems Engineering Vision 2020
• Design Framework– Model Based Engineering
Using modeling and simulation for rapid, concurrent, integrated system development and manufacturing
DoD Systems Engineering Vision 2020
• Adaptable DoD Systems– Capability on Demand
Real-time Adaptive SystemsRapidly Reconfigurable SystemsPre-planned Disposable Systems
Academia Needs
• Systems Architecting Laboratory: Real Engineering Problems and Customer
• Environment to demonstrate, value of systems engineering and new systems architecting approaches on real systems of various size
• Close cooperation with industry honoring propriety nature of information and data
• Dissemination channels for new research
Missouri S&T’s Approach
Systems Architecting Research
Smart System Architecting
• How can we assess architectures?• How can we represent architectures?• How can we generate architectures?• How can we reduce ambiguity hierarchically?• How can we test architectures for correctness? • What are the tools of architect?
Smart Systems Architecting
C. H. Dagli, A. Singh, J. P. Dauby, R. Wang, “Smart systems architecting: computational intelligence applied to trade space exploration and system design,” Systems Research Forum ,Vol. 3, No. 2 (2009) 101–119
DoD Systems Engineering Vision 2020
• Design Framework– Model Based Engineering
Using modeling and simulation for rapid, concurrent, integrated system development and manufacturing
Smart Systems Architecting1. What constitutes the “best” in architecture?2. What is the measure for comparing architectures?3. We can search for the “best” architecture, as long as we
can define “best”4. Can we associate an aggregate value in evaluating
functional architectures?5. How can we deal with the ambiguity of need requirements
and performance measures in the search process?6. Is there a way to mathematically represent functional
architectures?7. Can we generate architectures through a evolutionary
process?8. Can we integrate the architect in evolutionary architecting
process?C. H. Dagli, A. Singh, J. P. Dauby, R. Wang, “Smart systems architecting: computational intelligence applied to trade space exploration and system design,” Systems Research Forum ,Vol. 3, No. 2 (2009) 101–119
PERFORMANCE
SCHEDULE
RISK
PERCEPTIONS
COST
FACTS
What is the measure for comparing architectures?
Smart Systems Architecting
Adaptability
Affordability
Survivability
Robustness
Flexibility
Reliability
What is a reasonable approach to find and aggregate measure for comparing architectures?
Smart Systems Architecting
Super-Efficient , Eco-Friendly, and People Friendly
Top level system attributes
Smart Systems Architecting
Smart Systems Architecting (SSA) SSA Approach
Fuzzy Assessment and Computing with words
Evolutionary Algorithms for Architecture
Canonical Decomposition Fuzzy Comparison (CDFC)
Self Organizing Maps for Clustering Architecture Families
Models for Behavior Modeling
C. H. Dagli, A. Singh, J. P. Dauby, R. Wang, “Smart systems architecting: computational intelligence applied to trade space exploration and system design,” Systems Research Forum ,Vol. 3, No. 2 (2009) 101–119
Fuzzy Assessment and Computing with Words
A. Singh and C. H. Dagli, “"Computing with words" to support multi-criteria decision making during conceptual design,” Systems Research Forum, vol. 04, no. 01, p. 85, 2010.
Modern large-scale systems are comprised of many interacting subsystems and components and exhibit complex behavior.
This nonlinear behavior cannot be analyzed using traditional modeling approaches.
Fuzzy Cognitive Maps based methodology can be for assessing the inherent value of candidate architectures early in the design lifecycle.
Fuzzy Assessment and Computing with Words
A. Singh and C. H. Dagli, “"Computing with words" to support multi-criteria decision making during conceptual design,” Systems Research Forum, vol. 04, no. 01, p. 85, 2010.
The system and its components are represented in the form of a directional graph where the nodes represent system components and the arcs represent their interactions.
This modeling approach makes use of the “computing with words” (CW) paradigm to use human experience to assign linguistic weights to the arcs based on the strength of influence between connected nodes.
An overall value measure for a system can be derived by simulating the resulting graph. Such an approach will facilitate the selection of the best set of architectures or component technologies during the nascent design stages based on the value delivered to the stakeholder.
Evolutionary Algorithms for ArchitectureOnce architecture options have been identified using FCM and CW, evolutionary algorithms can be employed to find the right combination of technologies to utilize in a system design.
Functional architecture chromosome
Assess Threat
Assess Resource
Assess Status
PickWaveform
Generate Waveform
Amplify Waveform
Steer Antenna
Radiate Power
Gather Intel
Receive Signal
Library Lookup
Locate Target
Resolve Ambig
ThermManag
Prime Power
Blank/ EMI
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Canonical Decomposition Fuzzy Comparison (CDFC)The CDFC methodology is a new architecture assessment approach offering increased objectivity, fidelity, and defensibility in comparison to traditional approaches.
The methodology consists of four elements: •Extensible modeling – facilitates the exchange of data between model resolution levels.•Canonical design primitives – basic representations of system-component technologies.•Comparative analysis – comparison between heuristic and canonical embodiments.•Fuzzy inference – a mapping from system response features to fuzzy sets describing the architecture assessment.
J. P. Dauby, “Assessing system architectures: the canonical decomposition fuzzy comparative methodology,” Ph.D. dissertation, Dept. Eng. Management and Sys. Eng., Missouri University of Science and Technology, Rolla, MO, 2011.
Canonical Decomposition Fuzzy Comparison (CDFC)
Decomposition Using Canonical
Primitives
Comparative Analysis: Isolated vs. Integrated
Performance
Fuzzy Feature Interpretation
Architecture Assessment
1
2
3
4
Physical Architecture
1
3
2 4
Architecture assessment for airborne wireless systems in conjunction with a potential Acquisition Category (ACAT) ID program for the Department of the Navy
J. P. Dauby, “The canonical decomposition fuzzy comparative approach to assessing physical architectures,” INSIGHT, vol. 13, no. 3, pp. 60-62, Oct. 2010.
Self Organizing Maps for Clustering Architecture Families
Architecture solution candidates are described by functional, logical, or physical properties including integration sensitivity.
The set of properties for each candidate are used as the input vector to a variety of SOM algorithms.
The SOM output can identify design features and group potential architectural concepts into families based on common features, sensitivities, or tendencies.
This approach facilitates the development of architecture families that exhibit similar behavior as well as identify combinations of technologies that work well together.
Models for Behavior ModelingMotivation
Introduce dynamic model analysis into architecture modeling.
Facilitate system behavior, performance, and effectiveness analysis, architecture evaluation, and functionality verification and validation.
Renzhong Wang and Cihan H. Dagli, “Executable System Architecting Using Systems Modeling Language in Conjunction with Colored Petri Nets in a Model Driven Systems Development Process.” Journal of Systems Engineering, Vol. 14(3), 2011
Models for Behavior Modeling
Renzhong Wang and Cihan H. Dagli, “Executable System Architecting Using Systems Modeling Language in Conjunction with Colored Petri Nets in a Model Driven Systems Development Process.” Journal of Systems Engineering, Vol. 14(3), 2011
ModelingRequirement Analysis
and Specification
Requirements Analysis
Formal Model
SysML Diagrams
Executable model
CPN
Simulation
Interactive GUI
Architecture Analysis and Evaluation
Architecture refinement & reconfiguration
Functionality verification
Behavior analysis
Start
End
Model Transformation
Behavior as modeled
Behavior as modeled
Desired BehaviorDesired
Behavior
Refinement
External Application
• OMG (Object Management Group), Semantics of a Foundational Subset for Executable UML Models, Version 1.0 Beta 3, ptc/2010-03-14, http://www.omg.org/spec/FUML/1.0/Beta3/, 2010a
• Foundational UML Reference Implementation, http://portal.modeldriven.org/project/foundationalUML– Specify and demonstrate the semantics required to execute activity
diagrams and associated timelines per the SysML v1.0 specification – Specify the supporting semantics needed to integrate behavior with
structure and realize these activities in blocks and parts represented by activity partitions
Models for Behavior Modeling
Renzhong Wang and Cihan H. Dagli, “Executable System Architecting Using Systems Modeling Language in Conjunction with Colored Petri Nets in a Model Driven Systems Development Process.” Journal of Systems Engineering, Vol. 14(3), 2011
• “Behavioral Formalism” refers to a formalized framework for describing behavior, such as state machines, Petri nets, data flow graphs, etc.– UML/SysML, modeling language weak in executable
semantics– Supplemented by Semantics of a Foundational Subset for
Executable UML Models • Software that implemented behavioral formalism
– CORE, IBM Rational Rhapsody, CPN Tools, etc.
Models for Behavior Modeling
Renzhong Wang and Cihan H. Dagli, “Executable System Architecting Using Systems Modeling Language in Conjunction with Colored Petri Nets in a Model Driven Systems Development Process.” Journal of Systems Engineering, Vol. 14(3), 2011
• Combined usage of related tools. – Three basic functions of a model:
• Specification (of a system to be built), – UML and SysML
• Presentation (of a system to be explained to other people, or ourselves),– DoDAF products
• Simulation. – Petri nets, DEVS (Discrete Event Specification System – xUML, X
TUML, VM, Business Process Modeling Notation/Business Process Execution Language BPMN /BPEL
• Extract key information from simulation to support architecture evaluation and analysis.
• Refine the architecture design based on analysis results.
Models for Behavior Modeling
Renzhong Wang and Cihan H. Dagli, “Executable System Architecting Using Systems Modeling Language in Conjunction with Colored Petri Nets in a Model Driven Systems Development Process.” Journal of Systems Engineering, Vol. 14(3), 2011
DoDAF 2.0Architecture Viewpoints and DoDAF-described Models
DoD Architecture Framework Version 2.0 Volume I
Architecture Presentation Techniques
DoD Architecture Framework Version 2.0 Volume I
Architecture Analytics
DoD Architecture Framework Version 2.0 Volume I
Executable Modeling Formalisms
• The chosen of executable modeling language depends on the system to be modeled, the abstraction level to work on, and the system behavior of interest.
• Many modern distributed systems can be best specified by discrete event models because– The behavior of these systems is driven only by events that occur at discrete
time points.• Discrete-event models* represent the operation of a system as a
chronological discrete sequence of events. Each event occurs at an instant in time and marks a change of state in the system.
• An executable architecture is a dynamic model that defines the precise event sequences, the conditions under which event is triggered and information is produced or consumed, and the proprieties of producers, consumers and other resources associated with the operation of the system. * Banks, J. Discrete-event System Simulation. Pearson Prentice Hall, Upper Saddle River, NJ. 2005.
Colored Petri Nets (CPNs)Places carry makers, called tokens, which mark the state of a system.
Transitions describe the actions of the system
Arcs tell how actions modify the state and when they occur
1. Combining a well-defined mathematical foundation, an interactive graphical representation, and the capabilities to carry out simulations and formal verifications.
2. The same models can be used to check both the logical or functional correctness of a system and for performance analysis.
3. CPNs are very flexible in token definition and manipulation.
11`”data”
When certain conditions hold, transitions will be fired, causing a change in the placement of tokens and thus the change of system states.
Executable Semantics
Event Conditions Effects
TransitionPlace
(w tokens)
Place (w tokens)
TransitionState State
CPNCPN
Discrete Event System Specification
Discrete Event System Specification
SystemSystem
Output Data/Information Control signals Resources Other
•Time Delay•Post conditions
Input Data/Information Control signals Resources Other
Action /Activity (a set of actions)
Relationships between CPN Artifacts, System Entities and Discrete Event System Specifications
G I G
Net-Centric Architecture
Robust
Interoperable
Adaptable
Flexible Modular
System 1
Meta-Architecture
Dynamically Changing Meta-Architecture for Complex Systems
System 2
System 3
System 4
System n
System n-1
Models for Behavior Modeling
• For modeling the meta-architecture– Multi-agent based modeling
• Agents• Environment• Interactions
• For modeling sub-system architectures– Cognitive architectures
N. Kilicay-Ergin “Architecting System of Systems: Artificial Life Analysis of Financial Market Behavior”, PhD Dissertation Dept. Eng. Management and Sys. Eng., Missouri University of Science and Technology, Rolla, MO, 2007
Models for Behavior Modeling
Swarm Intelligence
ReinforcementLearning
Genetic Algorithm
Neural Networks
ComputationalIntelligenceToolbox
Short-termmemory
Long-termAssociative memory
Attention filter BiasImitation
MechanismModules
Reactive Mechanism
Deliberative Reasoning
Meta-managementP
erce
pti
on
Act
ion
Agent 1= System 1 Agent 2= System 2 Agent 3= System 3 Agent n= System n
CognitiveLevel*
AgentLevel
EnvironmentLevel
System Level Behavior
LearningClassifiers
Dynamics
Semantics Selection Criteria
System-of-Systems
Meta-architecture
Sub-system architectures
*Sloman’s H-Cogaff architecture,
2000
Missouri S&T’s Approach
Degrees and Graduate Certificates
Systems Engineering Degrees
• MS in Systems Engineering– Architected based on a need statement of invited Boeing RFP in 1998. – Since the inception of the program on Spring 2000 semester 410 engineers
have received their M.S. degrees. – Ten courses – six core and four engineering specialization- are required for
the degree.
• PhD in Systems Engineering– One graduate from Boeing Seattle out of four graduates since 2006– Fifteen students currently in the program
Curriculum Core Courses
Systems
Systems ArchitectureSysEng 469 – SystemsArchitecting
Systems Engineering and AnalysisSysEng 368 – SystemsEngr. and Analysis I
Systems Engineering – Information Based Design
SysEng 468 – SystemsEngr. and Analysis II
Complex Systems Management
Economic Decision AnalysisSysEng 413 Economic Analysis for Systems Engineering
Systems Engineering Mgt.SysEng 412 Complex Engineering Systems Program Mgt.
Organizational Behavior and Management
SysEng 411 Systems Engineering Capstone
Systems Engineering MS Degree
Systems Engineering Graduate Certificates
• Systems Engineering Graduate Certificate• Network Centric Graduate Certificate• Computational Intelligence Graduate Certificate• Model Based Systems Engineering Graduate Certificate (In
Approval Process )• Software Architecting and Engineering Graduate Certificate
SysEng 368 Systems Engineering and Analysis ISysEng 468 Systems Engineering and Analysis IISysEng 413 Economic Analysis for Systems EngineeringSysEng 469 Systems Architecting
Students completing these four courses with a minimum grade of B in each course are admitted to the M.S. degree program in Systems Engineering without taking the GRE.
Systems Engineering Graduate Certificate
Network Centric Systems Graduate CertificateCore Courses: • SysEng/CpE 419 Network-Centric Systems Architecting and Engineering
• CpE/SysEng 449 Network-Centric Systems Reliability and Security
Communications Engineering Elective Courses (select two):
• CpE 317 Fault Tolerant Digital Systems
• CpE 319 Digital Network Design
• CpE 349 Trustworthy, Survivable Computer Networks
• CpE/SysEng 348 Wireless Networks
• CpE /SysEng 443 Wireless Adhoc and Sensor Networks
• CpE 448 High Speed Networks
• CS 483 Computer Security
• CS 486 Mobile and Sensor Data Management
Core Courses:CpE 358/EE367/SysEng 367 Computational Intelligence
and select one of the following: CS 347 Introduction to Artificial IntelligenceCS 348 Evolutionary ComputingSysEng 378/CS 378/EE 368 Introduction to Neural Networks and Applications
Elective Courses (Select two courses not taken as a core course):EE/CpE/Sys Eng 301 Evolvable HardwareCS 347 Introduction to Artificial IntelligenceCS 348 Evolutionary ComputingCS 447 Advanced Topics in Artificial IntelligenceCS 448 Advanced Evolutionary ComputingSysEng/CpE/EE 458 Adaptive Critic DesignsCS/SysEng/CpE 404 Data Mining and Knowledge DiscoveryEE 337 Neural Networks for ControlSysEng 378/CS 378/EE 368 Introduction to Neural networks and ApplicationsCpE/SysEng/EE 457 Markov Decision ProcessesSysEng 478 Advanced Neural Networks
Computational Intelligence Graduate Certificate
SysEng 433 Distributed Systems ModelingSysEng 435 Model Based Systems EngineeringSysEng 479 Smart Engineering Systems DesignEmgt 374 Engineering Design Optimization
Model Based Systems Engineering Graduate Certificate
CS 308 Object Oriented Analysis and DesignCs 309 Software Requirements EngineeringSysEng 435 Model Based Systems EngineeringSysEng 470 Software Intensive Systems Architecting
Software Architecting and Engineering Graduate Certificate
Research Cooperation• DARPA Manufacturing Experimentation and Outreach (MENTOR) Program
supplier to Boeing Research and Technology- Awarded, Duration: One year• Department of Defense Systems Engineering Research Center- University
Affiliated Research Center SERC-UARC at Stevens Institute of Technology Project “Agile Systems Engineering: Experiential and Active Learning Approach”, Duration: 05/15/2010 to 7/31/2011
• Department of Defense University Affiliated Research Center for Systems Engineering Research Joint Proposal with Steven’s Institute of Technology, University of Southern California and other participating universities. October 2008 – October 2013
• The Boeing Company, Systems Engineering MS Degree Program for Italian Engineers: Under Industrial Return Project Italian 767 Tanker Transport, BOEING Industrial Participation Program Duration: 2006 – 2009
1. As an integrated global society, we depend on complex, distributed engineering systems that can adapt to the dynamically changing needs of society.
2. These systems are seen in health care, infrastructure, transportation, energy, defense, security, environmental, manufacturing, communications and supply chain systems, among others.
3. Adaptability within these systems is critical. We need to push the boundaries of research in Complex Adaptive Systems and respond to the continuous global change in systems needs.
http://complexsystems.mst.edu/
Future Research Needs
http://cser.mst.edu
Recent Publications1. Renzhong Wang and Cihan H. Dagli, “Executable System Architecting
Using Systems Modeling Language in Conjunction with Colored Petri Nets in a Model Driven Systems Development Process.” Journal of Systems Engineering, Article first published online: 4 March 2011
2. Dauby, J. P. Dagli, C. H. , "The Canonical Decomposition Fuzzy Comparative Methodology for Assessing Architectures," Systems Journal, IEEE , vol.5, no.2, pp.244-255, June 2011
3. Aaron A. Tucker, Gregory T. Hutto and Cihan H. Dagli “ Application of Design of Experiments to Flight Test: A Case Study” Journal of Aircraft Vol. 47, No.2,March-April 2010
4. Atmika Singh and Cihan H Dagli ““ Computing with words” to Support Multi-Criteria Decision-Making During Conceptual Design” Systems Research Forum Vol. 4, No. 1 (2010) 85-99.
Recent Publications• C.H. Dagli, Atmika Singh, Jason P. Dauby and Renzhong Wang “
Smart Systems Architecting: Computational Intelligence Applied to Trade Space Exploration and System Design”, Systems Research Forum Vol. 3, No. 2 (2009) 101–119.
• A.A. Tucker and C.H. Dagli, "Design of Experiments as a Means of Lean Value Delivery to the Flight Test Enterprise”, Journal of Systems Engineering, volume 12, Number 3, 2009. Pp. 201- 217.
• M. Rao, S. Ramakrishnan, and C. Dagli, “Modeling and simulation of net centric system of systems using systems modeling language and colored Petri-nets: A demonstration using the global earth observation system of systems,” Systems Engineering, vol. 11, 2008, pp. 203-220.
Most biological systems do not forecast or schedule They respond to their environment — quickly, robustly, and adaptively
As engineers, let us don’t try and control the system. Design the system so that it controls and adapts itself to the environment created by dynamically changing needs
Concluding Remarks
Are we there yet?