An Analytic Portfolio Approach to System of Systems Evolutions

Davendralingam, Navindran; DeLaurentis, Daniel A.

doi:10.1016/j.procs.2014.03.085

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…Uncertainty in the expected returns was addressed via interval data, thereby producing linear margins in the calculated capabilities. Note that, this model definition was similar to, and presumed to be a continuation of, a previous study by the same authors [67].…”

Section: Applications Of Portfolio Optimization In the Defence Sementioning

confidence: 69%

“…Moreover, the manuscript lacked many critical details needed to fully understand the proposed model. Davendralingam and DeLaurentis [67] considered optimization of the CVaR associated with a naval warfare case study using agent-based simulation in a SoS context. The objective was minimization of the CVaR (as described in Section II-A3) subject to a number of SoS network-related constraints.…”

Section: Applications Of Portfolio Optimization In the Defence Sementioning

confidence: 99%

“…Davendralingam and DeLaurentis [67] used Monte Carlo sampling to generate values for capability coefficients and covariances as well as to generate random portfolios for comparison.…”

Section: A Uncertaintymentioning

confidence: 99%

“…Alternatively, balance could be imposed as a constraint, as seen in [70]. In contrast, Davendralingam and DeLaurentis [67] argued that the concept of variance, which is typically assumed to follow a normal distribution, does not easily extend to the risk associated with defence applications where the complex inter-dependencies result in complicated joint distributions among the interacting agents.…”

Section: B Quantification Of Valuementioning

confidence: 99%

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Portfolio Optimization for Defence Applications

et al. 2020

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The problem of designing an effective future defense force is quite complex and challenging. One methodology that is often employed in this domain is portfolio optimization, whereby the objective is to select a diverse set of assets that maximize the return on investment. In the defense context, the return on investment is often measured in terms of the capabilities that the investments will provide. While the field of portfolio optimization is well established, applications in the defense sector pose unique challenges not seen in other application domains. However, the literature regarding portfolio optimization for defense applications is rather sparse. To this end, this paper provides a structured review of recent applications and identifies a number of areas that warrant further investigation.

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Section: Applications Of Portfolio Optimization In the Defence Sementioning

confidence: 69%

Section: Applications Of Portfolio Optimization In the Defence Sementioning

confidence: 99%

“…Davendralingam and DeLaurentis [67] used Monte Carlo sampling to generate values for capability coefficients and covariances as well as to generate random portfolios for comparison.…”

Section: A Uncertaintymentioning

confidence: 99%

Section: B Quantification Of Valuementioning

confidence: 99%

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Portfolio Optimization for Defence Applications

et al. 2020

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“…For example, tradespace analysis methods have been proposed that can handle different levels of model fidelity where architecture alternatives are evaluated in terms of their utility, cost, participation risk, and other value-sustaining "-ilities" [13], [14]. Others have investigated techniques for generating feasible SoS alternative architectures [15], optimizing architectures [16], [17], approximating SoS performance with surrogate models [18], and identifying important "-ilities" based on subjective input from decision-makers [19]. While these efforts support tradespace analysis, they do not provide direct means for assessing SoS robustness within those trade studies.…”

Section: Introductionmentioning

confidence: 99%

A Complex Network Framework for Validated Assessments of Systems of Systems Robustness

Markina‐Khusid

Jacobs

Antul

et al. 2022

IEEE Systems Journal

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The complexity of modern systems of systems (SoS) requires the ability to quickly and effectively evaluate robustness in architecture alternatives. This article presents a framework for rapid, quantitative comparisons of robustness in SoS architectures that leverages complex network methods for assessing robustness and design of experiments (DoE) techniques for validating their use in the scenario of interest. We consider both single-layer and multilayer network representations of SoS and focus on algebraic connectivity, inverse average path length, and largest connected component size as measures of robustness. Two case studies are used to illustrate our framework and assess its utility: a command, control, communications, computer, intelligence, surveillance, and reconnaissance (C4ISR) simulation and a multilayer message-passing network simulation. We find that most of the considered network metrics capture expected robustness trends, though their ability to capture these trends is often affected by the scenario of interest. These results demonstrate the potential value of complex network methods for lightweight analysis of robustness in SoS architecture alternatives, when appropriately supported by DoE methods for understanding their limitations. Index Terms-Complex networks, design of experiments, robustness, systems of systems, systems of systems architecture. I. INTRODUCTIONM ODERN systems are becoming increasingly networked to form systems of systems (SoS), providing novel capabilities across many domains. More specifically, an SoS is "a system-of-interest whose system elements are themselves systems; typically these entail large scale inter-disciplinary problems with multiple, heterogeneous, distributed systems" [1]. For example, consider a team of low-cost, heterogeneous autonomous systems with various sensors and data links working together to perform a surveillance mission. Other examples include intelligent transportation systems and smart cities utilizing data from a network of distributed sensors to improve operations. These networked systems have the potential to improve performance and efficiency relative to complex, monolithic systems of Manuscript

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A Network-Based Portfolio Optimization Approach for Military System of Systems Architecting

Yang

et al. 2018

IEEE Access

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An Analytic Portfolio Approach to System of Systems Evolutions

Cited by 7 publications

References 8 publications

Portfolio Optimization for Defence Applications

Portfolio Optimization for Defence Applications

A Complex Network Framework for Validated Assessments of Systems of Systems Robustness

A Network-Based Portfolio Optimization Approach for Military System of Systems Architecting

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