A Robust Portfolio Optimization Approach to System of System Architectures
A Robust Portfolio Optimization Approach to System of System Architectures
复制标题
系统架构系统的鲁棒组合优化方法
DOI：
10.1002/sys.21302
复制
发表时间：
2015
影响因子：
2
通讯作者：
D. DeLaurentis
中科院分区：
文献类型：
--
作者：
Navindran Davendralingam;D. DeLaurentis
军事能力作为系统的系统（so）的实现，在技术、操作和计划方面提出了重大的发展挑战。特别是，缺乏决定如何形成和发展考虑绩效和风险的SoS的工具。本研究利用金融工程和运筹学在投资组合优化方面的工具来协助在这种情况下做出决策。我们的方法通过一个框架来促进SoS体系结构的进化，该框架支持在进化过程的给定决策时期进行体系结构选择。该方法将相互依赖的系统的层次结构建模为网络上的一般节点，这些节点受连接性和兼容性约束，紧密地工作以实现总体能力目标。采用稳健的投资组合算法来解决数据不确定性、节点间性能和开发风险等固有的现实问题。海战场景演示了该方法从可用系统候选列表中查找系统“组合<e:1>”的应用。结果表明，该框架通过允许优化问题处理决策过程的数学密集方面，有效地降低了贸易空间探索的组合复杂性（例如，连通性规则、解决方案的可行性、解决方案的最优性）。因此，人类决策者可以在做出最终决策时专注于选择适当的风险规避权重。
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.