Computer-aided architecting of disaggregated space systems

This paper examines metaheuristic cost effectiveness optimization across collections of disaggregated space systems. Within the systems engineering community, there has been a realization that organizations optimize individual systems performance and manage system-specific cost, schedule and risks, with comparatively little regard to the holistic System-of-Systems (SOS) context. Optimization of individual satellite and homogeneous constellation design parameters have been well documented in literature. We expand extant techniques to the larger SoS context, with the potential to realize efficiencies by synthesizing and evaluating a larger design space. Using genetic algorithms, the method models a disaggregate space system as a combinatorial optimization problem. The combination of potential large satellites and small satellites are evaluated for cost effectiveness. Cost coefficients are derived from a variety of cost models, and overall effectiveness of a design alternative is provided through integrated simulation and optimization routines. We apply the proposed method to determine the optimal mix of space-based systems in an example remote sensing earth observation scenario.