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A Robust Portfolio Optimization Approach to System of System Architectures
Author(s) -
Davendralingam Navindran,
DeLaurentis Daniel. A.
Publication year - 2015
Publication title -
systems engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.474
H-Index - 50
eISSN - 1520-6858
pISSN - 1098-1241
DOI - 10.1002/sys.21302
Subject(s) - computer science , interdependence , portfolio , system of systems , process (computing) , robust optimization , risk analysis (engineering) , operations research , management science , systems engineering , mathematical optimization , systems design , software engineering , engineering , medicine , mathematics , political science , financial economics , law , economics , operating system
The realization of military capability as a System of Systems (SoS), presents significant development challenges across technical, operational and programmatic dimensions. In particular, tools for deciding how to form and evolve SoS which consider performance and risk are lacking. This research leverages tools from financial engineering and operations research perspectives in portfolio optimization to assist decision making in this setting. Our approach facilitates evolutions of SoS architecture through a framework that supports architecture selection at a given decision‐epoch of the evolutionary process. The approach models hierarchies of interdependent systems as generic nodes on a network that, subject to connectivity and compatibility constraints, work cohesively to fulfill overarching capability objectives. A robust portfolio algorithm is employed to address inherent real world issues of data uncertainty, inter‐nodal performance and developmental risk. A naval warfare scenario illustrates application of the method to find “portfoliosˮ of systems from a candidate list of available systems. Results show how the framework effectively reduces the combinatorial complexity of tradespace exploration (e.g., connectivity rules, feasibility of solutions, optimality of solutions) by allowing the optimization problem to handle the mathematically intensive aspects of the decision‐making process. As a result, human decision‐makers can focus on choosing the appropriate weights for risk aversion in making final decisions.