University of Maryland

Design Optimization with Imprecise Random Variables

Jeffrey W. Herrmann
Department of Mechanical Engineering and Institute for Systems Research
University of Maryland, College Park, MD 20742, USA


Design optimization is an important engineering design activity. Performing design optimization in the presence of uncertainty has been an active area of research. The approaches used require modeling the random variables using precise probability distributions or representing uncertain quantities as fuzzy sets. This work, however, considers problems in which the random variables are described with imprecise probability distributions, which are highly relevant when there is limited information about the distribution of a random variable. In particular, this paper formulates the imprecise probability design optimization problem and presents an approach for solving it. We present examples for illustrating the approach.

Copyright Notice: This paper will be presented at the 2009 SAE World Congress, April 20-23, 2009, Detroit, Michigan, and SAE has been assigned the copyright to this paper. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists or to reuse any copyrighted component of this work in other works must be obtained from the copyright holder.

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Last updated by Jeffrey W. Herrmann, January 9, 2009.

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