Computational optimization for design is effective, only to the extent that the aggregate objective function adequately captures designer's preference. Physical programming is an optimization method that captures the designer's physical understanding of the desired design outcome in forming the objective function. Furthermore, to be useful, a resulting optimal design must be sufficiently robust/insensitive to known and unknown variations that to different degrees affect the design's performance. This paper explores the effectiveness of the physical programming approach in explicitly addressing the issue of design robustness. Specifically, we synergistically integrate methods that had previously and independently been developed by the authors, thereby leading to optimal - robust - designs. We demonstrate that the Physical programming method can be used to efficiently address designer's preference in making tradeoffs between the mean and variance attributes when solving bi-objective robust design problems. The work documented in this paper establishes the superiority of the Physical programming method over the conventional weighted sum method in solving multiobjective optimization problems. It also illustrates that the Physical programming method is as efficient as other multicriteria mathematical programming techniques for the generation of Pareto solutions that belong to both convex and nonconvex efficient frontiers.
|Original language||English (US)|
|Number of pages||10|
|Journal||Collection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference|
|State||Published - Jan 1 1999|
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