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System of Systems Analytic Workbench

Abstract

The development of a large group of interdependently operating systems, or “System of Systems (SoS),” presents significant challenges across technical, operational, and programmatic dimensions. Trades between cost, schedule, performance, and associated spectrum of risks are essential during analysis of alternatives for both individual systems and the SoS architecture as a whole. The large number of decision variables involved, ubiquitous uncertainty and complex interactions that exist between systems creates analysis problems that go well beyond the immediate mental faculties of decision-makers. Often times, the decisions made focus on localized development at the systems level with little consideration for cascading effects on the bigger SoS picture. This chapter summarizes the development of a System of Systems Analytic Workbench (SoS AWB) that provides a set of computational tools to facilitate better-informed decision-making on evolving SoS architectures. The workbench motif is adopted since SoS practitioners typically generate archetypal technical queries that can be mapped to appropriate analysis methods best suited to provide outputs and insights directly relevant to posed questions. After an overview of the workbench framework, the methods currently available for use are presented along with their distinctive aspects in the concept of use. This chapter also describes initial applications of the workbench and knowledge gathered from the first users of the AWB tools.

Leads

Cesare Guariniello

Purdue University

Payuna Uday

Stevens Institute of Technology

Waterloo Tsutsui

Purdue University

Karen Marais

Purdue University

Publications

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  6. Dai , M. , Guariniello , C. , and DeLaurentis , D. ( 2022 ). Implementing a MOSA decision support tool in a model-based environment . In: Recent Trends and Advances in Model Based Systems Engineering (ed. A.M. Madni , B. Boehm , D. Erwin , et al.), 257 – 268 . Springer International Publishing .

  7. Davendralingam , N. and Delaurentis , D.A. ( 2015 ). A robust portfolio optimization approach to system of system architectures . Systems Engineering 18 ( 3 ): 269 – 283 .

  8. Davendralingam , N. , Guariniello , C. , and Delaurentis , D. ( 2018 ). A robust portfolio optimization approach using parametric piecewise linear models of system dependencies . In: Disciplinary Convergence in Systems Engineering Research (ed. A.M. Madni , B. Boehm , R.G. Ghanem , et al.), 83 – 96 . Springer International Publishing .

  9. Davendralingam , N. , Guariniello , C. , Tamaskar , S. et al. ( 2019 ). Modularity research to guide MOSA implementation . The Journal of Defense Modeling and Simulation 16 ( 4 ): 389 – 401 .

  10. Delaurentis , D.A. , Marais , K. , Davendralingam , N. , et al. ( 2017 ). System of systems analytic workbench toolset . Tools Available on Nanohub, Version 1.5 .

  11. DeLaurentis , D.A. , Moolchandani , K. , and Guariniello , C. ( 2022 ). System of Systems Modeling and Analysis . Boca Raton, FL : CRC Press .

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  13. Fang , Z. , Davendralingam , N. , and DeLaurentis , D. ( 2018 ). Multistakeholder dynamic optimization for acknowledged system-of-systems architecture selection . IEEE Systems Journal 12 ( 4 ): 3565 – 3576 .

  14. Fang , Z. and DeLaurentis , D. ( 2015 ). Multi-stakeholder dynamic planning of system of systems development and evolution . Conference on Systems Engineering Research (CSER) , Hoboken, NJ (17–19 March).

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  17. Guariniello , C. ( 2016 ). Supporting Space Systems Design via Systems Dependency Analysis Methodology . Purdue University .

  18. Guariniello , C. and DeLaurentis , D. ( 2013 ). Dependency analysis of system-of-systems operational and development networks . Procedia Computer Science 16 : 265 – 274 .

  19. Guariniello , C. and DeLaurentis , D. ( 2017 ). Supporting design via the system operational dependency analysis methodology . Research in Engineering Design 28 ( 1 ): 53 – 69 .

  20. Guariniello , C. , Fang , Z. , Davendralingam , N. et al. ( 2018 ). Tool suite to support model based systems engineering-enabled system-of-systems analysis . In: IEEE Aerospace Conference , 1 – 16 . Big Sky, MT .

  21. Guariniello , C. , Marsh , T.B. , Diggelmann , T. , and DeLaurentis , D.A. ( 2021 ). System-of-systems methods for technology assessment and prioritization for space architectures . In: IEEE Aerospace Conference (50100) , 1 – 13 . Big Sky, MT .

  22. Guariniello , C. , Mockus , L. , Raz , A.K. , and DeLaurentis , D.A. ( 2020 ). Towards intelligent architecting of aerospace system-of-systems: Part II . In: IEEE Aerospace Conference , 1 – 9 . Big Sky, MT .

  23. Guariniello , C. , Mockus , L. , Raz , A.K. , and DeLaurentis , D.A. ( 2019 ). Towards intelligent architecting of aerospace system-of-systems . In: IEEE Aerospace Conference , 1 – 11 . Big Sky, MT .

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  28. Shah , P. , Davendralingam , N. , and DeLaurentis , D.A. ( 2015 ). A conditional value-at-risk approach to risk management in system-of-systems architectures . In: System of Systems Engineering Conference (SoSE) , 457 – 462 . San Antonio, TX .

  29. Uday , P. and Marais , K.B. ( 2014 ). Resilience-based system importance measures for system-of-systems . Conference on Systems Engineering Research (CSER) , Redondo Beach, CA (21–22 March).

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The Systems Engineering Research Center (SERC) was established in the Fall of 2008 as a government-designated University Affiliated Research Center (UARC). The SERC has produced 15 years of research, focused on an updated systems engineering toolkit (methods, tools, and practices) for the complex cyber-physical systems of today and tomorrow.


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