A design representation is developed to model multi-attribute systems utilizing multi-dimensional clipping and transformation algorithms. Given a linear system characterization, three types of supporting information is generated for the decision maker: (1) a function matrix that describes the performance attributes dependent upon the decision variables; (2) a decision space that corresponds to the feasible decision set that meets performance requirements, and; (3) a performance space that represents the feasible performance region and the Pareto Optimal set. The analytical method developed for solving these feasible spaces is described for a linear system model. A case study is presented to demonstrate how to utilize the representation to locate a feasible solution and proceed to the desired trade-off of multiple attributes. Moreover, the potential incorporations of the representation with other influential design methodologies are discussed.
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