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

Li, Minghao; Li, Mengjun; Yang, Kewei; Xia, Boyuan; Wan, Chunqi

doi:10.1109/access.2018.2870654

Cited by 16 publications

(11 citation statements)

References 29 publications

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“…The unattended cluster architecture reflects the configuration of the components in the system and their interaction with the external environment [30], [31]. In the third part, a taskoriented flexible network is proposed given the possibility of node interruption in UAV cluster architecture and the uncertainties encountered at the task end, as well as the disadvantages of network architecture in terms of flexibility and self-adaptability.…”

Section: Flexible Network Organizational Structurementioning

confidence: 99%

Dynamic Tasks Scheduling Model of UAV Cluster Based on Flexible Network Architecture

Duan¹,

Wang²,

Wang³

et al. 2020

IEEE Access

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With the rapid growth of application demands and the real-time change of environmental situations, the defects of the UAV task network in adaptability, flexibility, and resilience are becoming more and more prominent. The current network architecture that the junction of points and lines is fixed cannot dynamically provide capacity requirements in real-time due to the failure nodes encountered in the Unmanned Aerial Vehicle (UAV) task scheduling process. To address this challenging issue, this paper proposes a flexible network architecture supporting dynamic fault-tolerant task scheduling model (DSM-FNA) for the UAV cluster. To be specific this paper resorts to super network theory, combining the management theory of flexible network and resilience network to carry out the organizational calculation on the model, and also draw upon linear transformation function to weight and stratify the capability value according to the ability requirement required by the task. Then, a flexible network architecture dynamic scheduling algorithm (FDSA) is proposed, and the substitution strategy is designed for the failure point, to realize the capability and dynamically adapt to the task. Finally, compared with the classical Max-Min algorithm and other algorithms, it is verified that the FDSA algorithm performs better dynamic adjustment for quick response in case of UAV cluster emergencies.

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Section: Flexible Network Organizational Structurementioning

confidence: 99%

Dynamic Tasks Scheduling Model of UAV Cluster Based on Flexible Network Architecture

Duan¹,

Wang²,

Wang³

et al. 2020

IEEE Access

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“…Li et al [19] proposed a portfolio optimization approach based on an SoS architecture. Solutions were evaluated with respect to both qualitative measures of effectiveness and quantitative measures of performance, variations of which were accounted for through the usage of scenarios.…”

Section: Applications Of Portfolio Optimization In the Defence Sementioning

confidence: 99%

“…While cost was typically considered in the form of one or more budgetary constraints, a few studies explicitly added the minimization of cost as an optimization objective [6], [15]- [18], [40], [43], [49], [51], [52], [74]. Another relatively common approach to valuation was to consider the balance, or variance, associated with the project selection as an objective [16], [19], [49], [51], [57], [74]. Balance/variance can also be considered a measure of robustness.…”

Section: B Quantification Of Valuementioning

confidence: 99%

“…Rather, it should be considered as an administrative tool with political repercussions [14]. Despite these known challenges and the importance of FFD, it is often reported that the literature regarding portfolio optimization for defense applications is rather scant [15]- [19]. This is especially true for applications that consider capability-oriented approaches.…”

Section: Introductionmentioning

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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“…Kangaspunta J et al [27] use the cost-efficiency method to decide the acquisition and maintenance of military equipment, aiming to build long-term capabilities in future military conflicts. Li et al [31] adopt a network-based method to formulate and analyze weapon system portfolio architecting problem by embedding different types of systems into a network. Zhou et al [32] study the evolving capability requirement-oriented portfolio planning problem with a capability-based approach from the perspective of operational research.…”

Section: Introductionmentioning

confidence: 99%

Model and Data-Driven System Portfolio Selection Based on Value and Risk

et al. 2019

Self Cite

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System portfolio selection is a kind of tradeoff analysis and decision-making on multiple systems as a whole to fulfill the overall performance on the perspective of System of Systems (SoS). To avoid the subjectivity of traditional expert experience-dependent models, a model and data-driven approach is proposed to make an advance on the system portfolio selection. Two criteria of value and risk are used to indicate the quality of system portfolios. A capability gap model is employed to determine the value of system portfolios, with the weight information determined by correlation analysis. Then, the risk is represented by the remaining useful life (RUL), which is predicted by analyzing time series of system operational data. Next, based on the value and risk, an optimization model is proposed. Finally, a case with 100 candidate systems is studied under the scenario of anti-missile. By utilizing the Non-dominated Sorting Differential Evolution (NSDE) algorithm, a Pareto set with 200 individuals is obtained. Some characters of the Pareto set are analyzed by discussing the frequency of being selected and the association rules. Through the conclusion of the whole procedures, it can be proved that the proposed model and data-driven approach is feasible and effective for system portfolio selection.

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

Cited by 16 publications

References 29 publications

Dynamic Tasks Scheduling Model of UAV Cluster Based on Flexible Network Architecture

Dynamic Tasks Scheduling Model of UAV Cluster Based on Flexible Network Architecture

Portfolio Optimization for Defence Applications

Model and Data-Driven System Portfolio Selection Based on Value and Risk

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