Constructing a general framework for systems engineering strategy
Systems Engineering
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The objective of this research is to develop a methodology and mathematical model to optimally select subsystems for subsequent integration into complex systems. This methodology is developed in accordance with systems engineering principals and processes. It advances modern trade study practices and processes toward product integration; specifically the activity of selecting subsystems at preliminary design for inclusion into larger systems. The systems developer is often faced with selecting subsystems available off-the-shelf (OTS) versus developing new subsystems specific to the application. Each approach has benefits and pitfalls, typically addressed in trade studies and detailed analyses. In practice, trades and analyses tend to be specific to single subsystems, and perhaps immediate interfacing subsystems. Factors considered include cost, schedule, technical performance and risk; but the dependencies between those factors, combined with multiple options for design versus OTS, make the subsystem selection process cumbersome when the goal is to consider all influencing factors within the entire system. This reality leads the developer to a narrow focus instead of approaching the product integration problem holistically. The methodology formulates the problem of subsystem selection in terms of a graph theoretic multi-objective model, conditioned for optimization based on enterprise goals. The model is visually represented as a network flow where nodes represent subsystems, and arcs represent subsystem interfaces. The interface parameters consist not of physical and functional relationships, but instead represent cost, schedule, technical performance, and risk. Those key technical and budgetary attributes are expressed mathematically in the model as optimization objectives. Mathematical formulation describes the complex system objectives, constraints, variables, and dependencies in terms of a multi-objective optimization model. In this model, parameters important to the product integration scheme are simultaneously quantified as part of the larger system. The solution set is a Pareto optimal list of alternative subsystems ranked in order of preference, allowing the developer a selection of subsystem combinations to meet technical performance requirements and satisfy budgetary goals. The methodology and mathematical model developed in this dissertation contribute to the greater good of the trade study discipline within the system engineering process. Specifically, complex systems are developed with less risk to redesign by correctly selecting the subsystem foundation at preliminary design. Complex systems, many of which are government funded, are developed to fill a societal need. Less financial waste on these major engineering projects is a benefit to society when the likelihood of reprogramming funds from other important projects is minimized. Completing complex system projects within cost and schedule constraints improves scheduled delivery to the public and diminishes funding loss to other essential projects.