Dynamic Planning of System of Systems Architecture Evolution

Fang, Zhemei; DeLaurentis, Daniel A.

doi:10.1016/j.procs.2014.03.055

Cited by 13 publications

(3 citation statements)

References 9 publications

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“…This allows us to set the planning horizon for the method used, which, as we can see from Figures 2 and 3, is shorter than the confidence planning horizon for individual parameters. It should be noted that the increase in the risk value is sharper compared to the assessment of individual parameters, which indicates that going beyond the horizon examined will almost certainly lead to management errors and the need for regular adjustment of plans (Fang and DeLaurentis, 2014).…”

Section: Resultsmentioning

confidence: 99%

The Risk Assessment Method in Prognostic Models of Production Systems Management with Account of the Time Factor

Mylnikov¹,

Kuetz²

2017

ERSJ

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The possibility of planning risks assessment and the setting of the planning horizon are analysed with account of the time factor basing. A risk metrics approach is used, with risks estimated as a time function given in tabular form for the purposes of master production. Dependence of the integrated planning risks on the accuracy of forecasts used is proved and a time point is identified after which the risk assessment value increases sharply. The study enables to obtain the values of planning risks integral assessment in management problems using forecast data for groups of indicators and parameters involved in decisionmaking.

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Section: Resultsmentioning

confidence: 99%

The Risk Assessment Method in Prognostic Models of Production Systems Management with Account of the Time Factor

Mylnikov¹,

Kuetz²

2017

ERSJ

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“…In the SoS context, Nusawardhana [31] employed ADP to a class of SoS problems that involves simultaneous design of a new system (e.g., aircraft) and allocation of the new system along with existing systems (e.g., airline fleet allocation) over a finite horizon. Fang and her coauthors [32][33][34] have also demonstrated the ability of ADP in supporting SoS evolution decision making for defense acquisition with proper assumptions. is paper continues leveraging the advantages of ADP in addressing complex multistage decision-making problems under uncertainty in the SoS context with more elements incorporated in the formulation.…”

Section: System-of-systems Evolutionmentioning

confidence: 99%

Architectural Models Enabled Dynamic Optimization for System-of-Systems Evolution

2020

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System of Systems (SoS) is designed to deliver value to participant stakeholders in a dynamic and uncertain environment where new systems are added and current systems are removed continuously and on their own volition. is requires effective evolution management at the SoS architectural level with adequate support of process, methods, and tools. is paper follows the principle of Model-Based Systems Engineering (MBSE) and develops a holistic framework integrating MBSE conceptual representations and approximate dynamic programming (ADP) to support the SoS evolution. e conceptual models provide a common architectural representation to improve communication between various decision makers while the dynamic optimization method suggests evolution planning decisions from the analytical perspective. e Department of Defense Architecture Framework (DoDAF) models using Systems Modeling Language (SysML) are used as MBSE artifacts to connect with ADP modeling elements through DoDAF metamodels to increase information traceability and reduce unnecessary information loss. Using a surface warfare SoS as an example, this paper demonstrates and explains the procedures of developing DoDAF models, mapping DoDAF models to ADP elements, formulating ADP formulation, and generating evolutionary decisions. e effectiveness of using ADP in supporting evolution to achieve a near-optimal solution that can maximize the SoS capability over time is illustrated by comparing ADP solution to other alternative solutions. e entire framework also sheds light on bridging the DoDAF-based conceptual models and other mathematical optimization methods.

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“…It is also crucial to include both the near-term and long-term objectives in the early design phase, planning in advance to improve the capability or reduce the cost in the long run. The Multi-Stakeholder Dynamic Optimization (MUSTDO) methodology, 23,24 as shown in the top graph of Fig. 3, supports the coordination of stakeholder conflict and inter-temporal conflict via an integration of transfer contract coordination mechanism and approximate dynamic programming (ADP).…”

Section: Multi-stakeholder Dynamic Optimization (Mustdo)mentioning

confidence: 99%

System‐of‐systems tools and techniques for the analysis of cyber‐physical systems

Guariniello

Raz

Fang

et al. 2020

Systems Engineering

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Dynamic and real-time adaptive configuration of Cyber-Physical Systems (CPSs) results in increased complexity due to a variety of heterogeneous and interdependent variables and creates unique challenges. For example, 1) Emergent Behavior: How do we ensure that system constituents dynamically and adaptively collaborate to produce a consistent repeatable functionality while supporting the capability to upgrade the individual entities through technology infusion; 2) Scale: How do we ensure scalability of these systems by managing complexity; 3) Risk Management: How do we evaluate and manage the risks associated with the connection and interdependencies of heterogenous systems.Design and development of this new generation of CPSs can be viewed through the lens of System-of-Systems (SoS) methodology which is designed to analyze and assess the evolving topologies created by interactions within a large complex system operating in dynamic and uncertain environment. In this paper, we propose the use of several SoS tools and techniques for the analysis and design of nextgeneration CPSs. Our SoS methodologies address features such as diversity of component systems, complex hierarchical structures, dynamic and emergent behavior, and interactions between components. Therefore, they are suitable to treat some of the challenging features of cyber-physical systems. However, it is necessary to modify these methodologies to address specific aspects of CPSs. Constraints and metrics from SoS methodology, applied to the design space, will support decision on component systems and the topology of their connections, and provide a set of -good designs‖, with desired characteristics.

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Dynamic Planning of System of Systems Architecture Evolution

Cited by 13 publications

References 9 publications

The Risk Assessment Method in Prognostic Models of Production Systems Management with Account of the Time Factor

The Risk Assessment Method in Prognostic Models of Production Systems Management with Account of the Time Factor

Architectural Models Enabled Dynamic Optimization for System-of-Systems Evolution

System‐of‐systems tools and techniques for the analysis of cyber‐physical systems

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