Discrete subsystem selection using linear physical programming

The design of complex systems may involve the selection of several subsystem designs. In this paper, we investigate the problem of selecting discrete concepts from multiple, coupled subsystems. This problem is one where both subsystem level (local) measures of merit and system level (global) measures of merit are present. We develop an approach to obtain the sets of subsystem design concepts that will satisfy the system objectives. 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. Discrete optimization techniques from graph theory and linear physical programming are combined to form a powerful algorithm to solve this problem. The approach presented in this paper can be used by a designer to decrease the number of subsystem combinations that represent successful system design alternatives. Such a tool can be most useful at the conceptual design stage of the design process where the number of design alternatives is potentially very high and the need for identifying successful subsystem design combinations arises. Once the promising subsystem designs are obtained at the conceptual design stage, focus can be restricted on these chosen design alternatives for further testing and refinement at a later embodiment design stage.