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Portfolio Management and Optimization for System of Systems

Abstract

Across commercial, government, and defense sectors, realization of new capabilities is more commonly happening through the integration of existing components, seeking a new emergent behavior. One driving factor behind this is the desire to reduce the time to fielding. This pursuit of integrating existing components and systems to produce new capabilities inherently increases the complexity of the design problem, as interactions between elements can lead to unexpected behaviors and effects. Furthermore, individual systems continue to increase in complexity, often due to increased reliance on software. All of these concerns are increasing the difficulty of meeting budgetary and schedule constraints. This chapter provides an overview of emerging research and development in the field of portfolio optimization, visualization techniques, and example implementations to educate the reader on the advancements to the state of practice in portfolio management and optimization in a mission engineering or system of systems context.

Leads

Frank Patterson

Georgia Tech Research Institute

David Fullmer

Georgia Tech Research Institute

Daniel Browne

Georgia Tech Research Institute

Santiago Balestrini-Robinson

Georgia Tech Research Institute

Publications

  1. Azmi , R. and Tamiz , M. ( 2010 ). A review of goal programming for portfolio selection . In: New developments in Multiple Objective and Goal Programming (ed. D. Jones , M. Tamiz , and J. Ries ), 15 – 33 . Berlin, Heidelberg : Springer .

  2. Best , M.J. ( 2010 ). Portfolio Optimization . CRC Press .

  3. Bertsimas , D. and Sim , M. ( 2004 ). The price of robustness . Operations Research 52 ( 1 ): 35 – 53 .

  4. Beume , N. , Naujoks , B. , and Emmerich , M. ( 2007 ). SMS-EMOA: multiobjective selection based on dominated hypervolume . European Journal of Operational Research 181 ( 3 ): 1653 – 1669 .

  5. Coello , C.A.C. , Lamont , G.B. , and Van Veldhuizen , D.A. ( 2007 ). Evolutionary Algorithms for Solving Multi-objective Problems (ed. D.E. Goldberg and J.R. Koza ). New York : Springer .

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  7. Davendralingam , N. and DeLaurentis , D. ( 2013 ). A robust optimization framework to architecting system of systems . Procedia Computer Science 16 : 255 – 264 .

  8. Deb , K. , Pratap , A. , Agarwal , S. , and Meyarivan , T.A.M.T. ( 2002 ). A fast and elitist multiobjective genetic algorithm: NSGA-II . IEEE Transactions on Evolutionary Computation 6 ( 2 ): 182 – 197 .

  9. Deb , K. and Jain , H. ( 2013 ). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints . IEEE Transactions on Evolutionary Computation 18 ( 4 ): 577 – 601 .

  10. Grandjean , M . ( 2014 ). Historical Data Visualization: Minard's map vectorized and revisited .

  11. Jain , H. and Deb , K. ( 2013 ). An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part II: handling constraints and extending to an adaptive approach . IEEE Transactions on Evolutionary Computation 18 ( 4 ): 602 – 622 .

  12. Hwang , C.L. and Masud , A.S.M. ( 2012 ). Multiple Objective Decision Making—Methods and Applications: A State-of-the-Art Survey , vol. 164 . Springer Science & Business Media .

  13. Navon , A. , Shamsian , A. , Chechik , G. , and Fetaya , E. ( 2020 ). Learning the Pareto front with hypernetworks . The Ninth International Conference on Learning Representations .

  14. Marler , R.T. and Arora , J.S. ( 2004 ). Survey of multi-objective optimization methods for engineering . Structural and Multidisciplinary Optimization 26 : 369 – 395 .

  15. Mavrotas , G. ( 2009 ). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems . Applied Mathematics and Computation 213 ( 2 ): 455 – 465 .

  16. Mueller-Gritschneder , D. , Graeb , H. , and Schlichtmann , U. ( 2009 ). A successive approach to compute the bounded Pareto front of practical multiobjective optimization problems . SIAM Journal on Optimization 20 ( 2 ): 915 – 934 .

  17. Panichella , A. ( 2019 ). An adaptive evolutionary algorithm based on non-Euclidean geometry for many-objective optimization . In: Proceedings of the Genetic and Evolutionary Computation Conference (ed. M. López-Ibáñez ), 595 – 603 . New York : Association for Computing Machinery .

  18. Przybylski , A. and Gandibleux , X. ( 2017 ). Multi-objective branch and bound . European Journal of Operational Research 260 ( 3 ): 856 – 872 .

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  20. Rosenthal , R.E. ( 1985 ). Concepts, theory, and techniques principles of multiobjective optimization . Decision Sciences 16 ( 2 ): 133 – 152 .

  21. Santiago , A. , Huacuja , H.J.F. , Dorronsoro , B. et al. ( 2014 ). A survey of decomposition methods for multi-objective optimization . In: Recent Advances on Hybrid Approaches for Designing Intelligent Systems (ed. O. Castillo , P. Melin , W. Pedrycz , and J. Kacprzyk ), 453 – 465 . Cham : Springer .

  22. Section 809 Panel ( 2019 ). Report of the Advisory Panel on Streamlining and Codifying Acquisition Regulations . Streamlining and Codifying Acquisition, Volume 3 of 3.

  23. Thomas , J.J. and Cook , K.A. ( 2006 ). A visual analytics agenda . IEEE Computer Graphics and Applications 26 ( 1 ): 10 – 13 .

  24. Von Winterfeldt , D. and Fischer , G.W. ( 1975 ). Multi-attribute utility theory: models and assessment procedures . In: Utility, Probability, and Human Decision Making: Selected Proceedings of an Interdisciplinary Research Conference , Rome (3–6 September 1973) (ed. D. Wendt and C. Vlek ), 47 – 85 . Dordrecht, Netherlands : Springer .

  25. Zanakis , S.H. , Solomon , A. , Wishart , N. , and Dublish , S. ( 1998 ). Multi-attribute decision making: a simulation comparison of select methods . European Journal of Operational Research 107 ( 3 ): 507 – 529 .

  26. Zhang , Q. and Li , H. ( 2007 ). MOEA/D: a multiobjective evolutionary algorithm based on decomposition . IEEE Transactions on Evolutionary Computation 11 ( 6 ): 712 – 731 .

  27. Zhou , A. , Qu , B.Y. , Li , H. et al. ( 2011 ). Multiobjective evolutionary algorithms: a survey of the state of the art . Swarm and Evolutionary Computation 1 ( 1 ): 32 – 49 .

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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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