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Robust design is a methodology for improving the quality of a product or process by minimizing the effect of variations in the inputs without eliminating the causes of those variations. In robust design, the putative best design is obtained by solving a multi-criteria optimization problem, trading off the nominal performance against the minimization of the variation of the performance measure. Because some existing methods combine the two criteria with a weighted sum or another fixed aggregation strategy, which are known to miss Pareto points, they may fail to obtain a desired design. To overcome this inadequacy, a more comprehensive preference aggregation method is implemented here into robust design. Three examples -- one simple mathematical example, one multi-criteria structure design example, and one automotive example -- are presented to illustrate the effectiveness of the proposed method.

There are two sorts of uncertainty inherent in engineering design, the random and the epistemic. Random, or stochastic, uncertainty deals with the randomness or predictability of an event. It is well understood, easily modeled using classical probability, and ideal for such uncertainties as variations in manufacturing processes or material properties. Epistemic uncertainty deals with our lack of knowledge, our lack of information, and our own and others' subjectivity concerning design parameters. Epistemic uncertainty plays a particularly important role in the early stages of engineering design, when a lack of information about nominal values of parameters is much more important than potential variations in those parameters. Design reuse, or the design of product platforms, is an example in which epistemic uncertainty can play a crucial role in early design.