Model‐based architecture and programmatic optimization for satellite system‐of‐systems architectures

Abstract

This paper presents a new approach to architecture optimization for an enterprise or System of Systems (SoS) with the goal of enhancing individual system acquisition programs and the operational effectiveness of the enterprise as a whole. Replacing the current common practice of utilizing one or two technical parameters serially to evaluate candidate architectures is a better method that employs a fuller set of technical and programmatic variables in a Model-Based Systems Engineering (MBSE) context. Integrated architecture modelling and optimization is performed in a tool-supported MBSE process to identify an optimum architecture solution. This specifically addresses the predesign phases of a system acquisition program where leverage on total program cost is the greatest. We seek to improve the ability to deal with challenges such as management independence, technical diversity, and lack of synchronization across the programs involved in a SoS. This implementation results in a higher quality optimization with a more informed, and traceable selection decision. An example satellite communications SoS case study is used to develop the approach and highlight its utility. 372

Utilizing a maximin optimization approach to maximize system resiliency

“…37 A genetic algorithm is capable of solving these complex problems in a relatively efficient manner due to its extreme parallelizability. 38 In order to utilize the genetic algorithm as described above, it is necessary to have a quantifiable fitness function for both the system (such as the resilience metrics discussed in Section 2) and the operating environments (e.g., an inverse of the resilience metrics discussed in Section 2). Additionally, the fitness function can only have a scalar output-restricting the usage of multiple criteria optimization.…”

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

“…A few researchers have started exploring the integration of analytical methods and model-based architectures. For instance, LaSorda et al [9] combined model-based systems engineering with programmatic optimization for satellite SoS architecting. Guariniello et al [10] took the initial step to connect a few analytical methods for SoS development and DoDAF models.…”

“…On the one hand, the DoDAF-based conceptual models help to identify the key elements determining the consistent performance delivery during SoS evolution; on the other hand, the ADP method provides sequential decision support from the analytical perspective based on the key information extracted from the DoDAF models. Initial attempts to connect the conceptual models and optimization methods have been published in some recent papers [9][10][11], but significant efforts, such as a clearer mapping between DoDAF models and mathematical elements, are still required. Overall, this paper aims to contribute to the SoS research by developing a comprehensive framework that links the architectural models and dynamic optimization method based on the steps in the "wave model" to generate effective decision support for SoS evolution.…”

Application of Model-Based Systems Engineering Concepts to Support Mission Engineering

“…Maheshwari et al [18] develop integrated SoS Analytics Workbench capabilities tied to MBSE artifacts to conduct SysML-based analysis through simulation. LaSorda et al [19] develop a model-based architecture to optimize coordinated satellite deployment, the findings of which are generalized and presented as a method for effective MBSE in [20]. These recent efforts have expanded the conceptualization of MBSE and demonstrated the applicability and impact that it may have for the development of a broad range of systems.…”