Modeling Relative Importance of Design Criteria With a Modified Pareto Preference

[+] Author and Article Information
Brian J. Hunt, Margaret M. Wiecek

Department of Mathematical Sciences,  Clemson University, Clemson, SC

Vincent Y. Blouin

Department of Mechanical Engineering,  Clemson University, Clemson, SC

J. Mech. Des 129(9), 907-914 (Dec 16, 2006) (8 pages) doi:10.1115/1.2747634 History: Received January 09, 2006; Revised December 16, 2006

Engineering design problems are studied within a multicriteria optimization and decision-making framework. A methodology is developed that modifies the traditional Pareto preference to model designer’s preferences reflected in the relative importance of criteria. The intent is to reduce the number of candidate designs to facilitate the selection of a preferred design. The versatility of this preference model allows it to be incorporated into the problem solution process either a priori, a posteriori, or iteratively, each offering different advantages. In the a priori approach, all Pareto efficient designs that do not satisfy the designer’s preferences are never computed. In the a posteriori approach, a set of Pareto efficient designs is computed and then easily reduced based on the designer’s preferences. Finally, the iterative approach offers the ability to adjust the designer’s preferences by exploring their impact on the reduction of the Pareto efficient design set. The methodology is based on the concepts of convex cones and allowable tradeoff values between criteria. The theoretical foundation of the preference model is presented in the context of engineering design and the methodology is illustrated using a bi-criteria structural design problem and a tri-criteria vehicle dynamics design problem.

Copyright © 2007 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.



Grahic Jump Location
Figure 1

Example of outcome space with Pareto and preference cones for a bi-criteria minimization problem

Grahic Jump Location
Figure 3

Sets of outcomes with various preferences; (a) Pareto set (no preference), (b) Method 1 (a priori), (c) Method 2 (a posteriori)

Grahic Jump Location
Figure 4

Set of Pareto outcomes (dots), nondominated outcomes for preference A3 (circles), and baseline (cross)

Grahic Jump Location
Figure 5

Number of remaining Pareto efficient designs after filtering for various allowable tradeoff values



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In