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Preference-directed design

Published online by Cambridge University Press:  27 February 2009

Joseph G. D’Ambrosio  and
William P. Birmingham
Joseph G. D’Ambrosio
Affiliation:
Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, MI 48109
William P. Birmingham
Affiliation:
Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, MI 48109
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Abstract

Current design practices mandate that engineering designs be evaluated based on multiple attributes, e.g., cost, power, and area. For multiattribute design problems, generation and evaluation of the Pareto optimal set guarantees the optimal design will be found, but is not practical for a large class of problems. Iterative techniques can be applied to most problems, but sacrifice optimality. In this paper, we introduce a new technique that extends the set of design problems that can be solved optimally. By first constructing an imprecise value function, the number of nondominated alternatives that must be generated is reduced. We describe an implementation based on combinatorial optimization and constraint satisfaction which achieves additional performance gains by decomposing the value function to identify dominated design-variable assignments. Test results indicate that our approach extends the set of problems that can be solved optimally.

Keywords

DesignOptimizationUtility TheoryConstraint Satisfaction

Information

Type
Articles
Information
AI EDAM , Volume 9 , Issue 3 , June 1995 , pp. 219 - 230
Copyright
Copyright © Cambridge University Press 1995

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