1. MISSOURI UNIVERSITY OF SCIENCE AND TECHNOLOGY Rolla, Missouri, U.S.A.
Systems Engineering Research Taking Systems Engineering to the Next Level
Cihan H Dagli, PhD
Professor 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 TECHNOLOGY Rolla, Missouri, U.S.A.

2. Outline Introduction Academia Needs Missouri S&T’s Approach
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

3. 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)

4. 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.

5. Introduction 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
Courtesy of Dr. Mike McCoy

6. Introduction
(Adopted from An Overview of Global Earth Observation System of Systems (GEOSS), Stefan Falke, Geospatial Intelligence Operating Unit, Northrop Grumman Corporation)

7. Introduction Trans-national Manufacturing
Super-Efficient , Eco-Friendly, and People Friendly
Trans-national Manufacturing

8. 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)

9. Need for Systems Architecting and Engineering
Cost and Schedule Performance as a Function of Systems Engineering Effort
*Source: INCOSE Systems Engineering Center of Excellence SECOE INCOSE 2003; & Honour, E. “Understanding Value of Systems Engineering”, INCOSE Conference, June 20-24, 2004

10. Need for Systems Architecting and Engineering
Performed by NDIA in conjunction with the SEI in
Surveyed 64 projects at defense contractors to assess:
Data was collected anonymously to encourage honest and accurate reporting.
Results published at:
ocuments/08.reports/08sr034.html
*Source: Presentation of Joe Elm from Software Engineering Institute Carnegie Mellon University

11. Need for Systems Architecting and Engineering
PROJECT PERFORMANCE vs. TOTAL SE CAPABILITY
39%
46%
15%
29%
59%
12%
31%
13%
56%
Best
Performance
( x > 3.0 )
Moderate
( 2.5  x  3.0 )
Lower
( x < 2.5 )
Lower Capability
( x  2.5 )
N = 13
Moderate Capability
( 2.5 < x < 3.0 )
N = 17
Higher
Capability
(x  3.0 )
N = 16
Gamma = 0.32
p = 0.04
1.00
0.75
0.50
0.25
0.00
*Source: Presentation of Joe Elm from Software Engineering Institute Carnegie Mellon University

12. Need for Systems Architecting and Engineering
*Source: Presentation of Joe Elm from Software Engineering Institute Carnegie Mellon University

13. Need for Systems Architecting and Engineering
*Source: Presentation of Joe Elm from Software Engineering Institute Carnegie Mellon University

14. 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

15. DoD Systems Engineering Vision 2020
Design Principles
Platform Based Engineering Using a common core platform to develop many related systems/capabilities
Trusted System Design Developing trusted systems from untrusted components

16. DoD Systems Engineering Vision 2020
Design Framework
Model Based Engineering Using modeling and simulation for rapid, concurrent, integrated system development and manufacturing

17. DoD Systems Engineering Vision 2020
Adaptable DoD Systems
Capability on Demand Real-time Adaptive Systems Rapidly Reconfigurable Systems Pre-planned Disposable Systems

18. 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

19. Missouri S&T’s Approach Systems Architecting Research

20. 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?

21. 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

22. DoD Systems Engineering Vision 2020
Design Framework
Model Based Engineering Using modeling and simulation for rapid, concurrent, integrated system development and manufacturing

23. Smart Systems Architecting
What constitutes the “best” in architecture?
What is the measure for comparing architectures?
We can search for the “best” architecture, as long as we can define “best”
Can we associate an aggregate value in evaluating functional architectures?
How can we deal with the ambiguity of need requirements and performance measures in the search process?
Is there a way to mathematically represent functional architectures?
Can we generate architectures through a evolutionary process?
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

24. Smart Systems Architecting
PERFORMANCE
PERCEPTIONS
SCHEDULE
COST
RISK
FACTS
What is the measure for comparing architectures?

25. Smart Systems Architecting
Adaptability
Affordability
Survivability
Robustness
Flexibility
Reliability
What is a reasonable approach to find and aggregate measure for comparing architectures?

26. Smart Systems Architecting
Super-Efficient , Eco-Friendly, and People Friendly
Top level system attributes

27. 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

28. Fuzzy Assessment and Computing with Words
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.
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,  

29. Fuzzy Assessment and Computing with Words
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.
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,  

30. Evolutionary Algorithms for Architecture
Once 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

31. 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.

32. Canonical Decomposition Fuzzy Comparison (CDFC)
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 , Oct

33. 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.

34. Models for Behavior Modeling
Motivation
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

35. Models for Behavior Modeling
Requirement 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
Desired Behavior
Refinement
External Application
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

36. Models for Behavior Modeling
OMG (Object Management Group), Semantics of a Foundational Subset for Executable UML Models, Version 1.0 Beta 3, ptc/ , a
Foundational UML Reference Implementation,
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
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

37. Models for Behavior Modeling
“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.
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

38. Models for Behavior Modeling
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, XTUML, 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.
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

39. DoDAF 2.0 Architecture Viewpoints and DoDAF-described Models
DoD Architecture Framework Version 2.0 Volume I

40. Architecture Presentation Techniques
DoD Architecture Framework Version 2.0 Volume I

41. Architecture Analytics
DoD Architecture Framework Version 2.0 Volume I

42. 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

43. Colored Petri Nets (CPNs)
Places carry makers, called tokens, which mark the state of a system.
Transitions describe the actions of the system
When certain conditions hold, transitions will be fired, causing a change in the placement of tokens and thus the change of system states.
Arcs tell how actions modify the state and when they occur 1
1`”data”
A binding Transition is a transition that is ready to fire. It requires there are sufficient tokens in each of the input places and the tokens match the arc expressions (Inscriptions)
CPNs offer an advantage of combining
Combining a well-defined mathematical foundation, an interactive graphical representation, and the capabilities to carry out simulations and formal verifications.
The same models can be used to check both the logical or functional correctness of a system and for performance analysis.
CPNs are very flexible in token definition and manipulation.

44. Executable Semantics Place (w tokens) Place (w tokens) CPN Transition
System
Conditions
Event
Effects
Input
 Data/Information
 Control signals
 Resources
 Other
Action /
Activity (a set of actions)
Output
 Data/Information
 Control signals
 Resources
 Other
Time Delay
Post conditions
Discrete Event System Specification
Transition
State
State
Relationships between CPN Artifacts, System Entities and Discrete Event System Specifications

45. Models for Behavior Modeling
System 1
Meta-Architecture
System 4
G I G
Net-Centric Architecture
Robust
Interoperable
Adaptable
Flexible
Modular
System 2
System
n-1
System 3
System n
Dynamically Changing Meta-Architecture for Complex Systems

46. 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

47. Deliberative Reasoning
Swarm
Intelligence
Reinforcement
Learning
Genetic
Algorithm
Neural
Networks
Computational
Toolbox
Short-term
memory
Long-term
Associative memory
Attention filter
Bias
Imitation
Mechanism
Modules
Reactive Mechanism
Deliberative Reasoning
Meta-management
Perception
Action
Agent 1= System 1
Agent 2= System 2
Agent 3= System 3
Agent n= System n
Cognitive
Level*
Agent
Level
Environment
System Level Behavior
Classifiers
Dynamics
Semantics
Selection Criteria
System-of-Systems
Meta-architecture
Sub-system architectures
*Sloman’s H-Cogaff architecture, 2000

48. Missouri S&T’s Approach Degrees and Graduate Certificates

49. 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

50. Systems Engineering MS Degree
Curriculum
Core Courses
Systems
Systems Architecture
SysEng 469 – Systems Architecting
Systems Engineering and Analysis
SysEng 368 – Systems
Engr. and Analysis I
Systems Engineering – Information Based Design
SysEng 468 – Systems
Engr. and Analysis II
Complex Systems Management
Economic Decision Analysis
SysEng 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

51. Systems Engineering Graduate Certificates
Network Centric Graduate Certificate
Computational Intelligence Graduate Certificate
Model Based Systems Engineering Graduate Certificate (In Approval Process )
Software Architecting and Engineering Graduate Certificate

52. Systems Engineering Graduate Certificate
SysEng 368 Systems Engineering and Analysis I SysEng 468 Systems Engineering and Analysis II SysEng 413 Economic Analysis for Systems Engineering SysEng 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.

53. Network Centric Systems Graduate Certificate
Core 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

54. Computational Intelligence Graduate Certificate
Core Courses:
CpE 358/EE367/SysEng 367 Computational Intelligence and select one of the following: 
CS 347 Introduction to Artificial Intelligence
CS 348 Evolutionary Computing
SysEng 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 Hardware
CS 447 Advanced Topics in Artificial Intelligence
CS 448 Advanced Evolutionary Computing
SysEng/CpE/EE 458 Adaptive Critic Designs
CS/SysEng/CpE 404 Data Mining and Knowledge Discovery
EE 337 Neural Networks for Control
SysEng 378/CS 378/EE 368 Introduction to Neural networks and Applications
CpE/SysEng/EE 457 Markov Decision Processes
SysEng 478 Advanced Neural Networks

55. Model Based Systems Engineering Graduate Certificate
SysEng 433 Distributed Systems Modeling
SysEng 435 Model Based Systems Engineering
SysEng 479 Smart Engineering Systems Design
Emgt 374 Engineering Design Optimization

56. Software Architecting and Engineering Graduate Certificate
CS 308 Object Oriented Analysis and Design
Cs 309 Software Requirements Engineering
SysEng 435 Model Based Systems Engineering
SysEng 470 Software Intensive Systems Architecting

57. 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

58. Future Research Needs
As an integrated global society, we depend on complex, distributed engineering systems that can adapt to the dynamically changing needs of society.
These systems are seen in health care, infrastructure, transportation, energy, defense, security, environmental, manufacturing, communications and supply chain systems, among others.
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.

59. Recent Publications
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
Dauby, J. P. Dagli, C. H. , "The Canonical Decomposition Fuzzy Comparative Methodology for Assessing Architectures," Systems Journal, IEEE , vol.5, no.2, pp , June 2011
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
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)

60. 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, Pp
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

61. Concluding Remarks
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

62. Are we there yet?