Title:
Computational Methods for Decision Making
Computational Methods for Decision Making
Author(s)
Bruns, Morgan Chase
Paredis, Christiaan J. J.
Ferson, Scott
Paredis, Christiaan J. J.
Ferson, Scott
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Abstract
In this paper, we investigate computational methods for decision making based on
imprecise information in the context of engineering design. The goal is to identify the subtleties
of engineering design problems that impact the choice of computational solution methods, and to
evaluate some existing solution methods to determine their suitability and limitations. Although
several approaches for propagating imprecise probabilities have been published in the literature,
these methods are insufficient for practical engineering analysis. The dependency bounds
convolution approach of Williamson and Downs and the distribution envelope determination
approach of Berleant work sufficiently well only for open models (that is, models with known
mathematical operations). Both of these approaches rely on interval arithmetic and are therefore
limited to problems with few repeated variables. In an attempt to overcome the difficulties faced
by these deterministic methods, we propose an alternative approach that utilizes both Monte
Carlo simulation and optimization. The Monte Carlo/optimization hybrid approach has its own
drawbacks in that it assumes that the uncertain inputs can be parameterized, that it requires the
solution of a global optimization problem, and that it assumes independence between the
uncertain inputs.
Sponsor
National Science Foundation (Grant #DMI-0522116)
Date Issued
2006-02
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