System design through subsystem selection using physical programming

The design of complex systems can involve the selection of several subsystem designs. We investigate the problem of selecting discrete concepts from multiple, coupled subsystems. This problem is one where measures of merit for both subsystem (local) and system (global) levels are present. An approach is developed to obtain the sets of preferred subsystem design concepts. Graph theory is used to represent the coupled selection problem where the nodes of the graph are the subsystem design choices and the arcs connecting the nodes indicate the relationships between the subsystems. Optimization techniques from graph theory and physical programming are combined to form an approach to model and solve this problem. This approach can be used to identify a given number of successful, or feasible, subsystem combinations that represent design alternatives. Once the promising subsystem designs are obtained at the conceptual design stage, focus can be restricted to these chosen design alternatives for further testing and refinement at a later embodiment design stage. Although the examples presented in this paper involve conceptual design, the presented approach can be used with any coupled discrete selection problem.