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RESEARCH PAPERS

Product Design Selection With Preference and Attribute Variability for an Implicit Value Function

[+] Author and Article Information
A. K. Maddulapalli

Department of Mechanical Engineering, University of Maryland, College Park, MD 20742

S. Azarm

Department of Mechanical Engineering, University of Maryland, College Park, MD 20742azarm@umd.edu

J. Mech. Des 128(5), 1027-1037 (Nov 04, 2005) (11 pages) doi:10.1115/1.2216728 History: Received January 21, 2005; Revised November 04, 2005

An important aspect of engineering product design selection is the inevitable presence of variability in the selection process. There are mainly two types of variability: variability in the preferences of the decision maker (DM) and variability in attribute levels of the design alternatives. We address both kinds of variability in this paper. We first present a method for selection with preference variability alone. Our method is interactive and iterative and assumes only that the preferences of the DM reflect an implicit value function that is differentiable, quasi-concave and non-decreasing with respect to attributes. The DM states his/her preferences with a range (due to the variability) for marginal rate of substitution (MRS) between attributes at a series of trial designs. The method uses the range of MRS preferences to eliminate “dominated designs” and then to find a set of “potentially optimal designs.” We present a payload design selection example to demonstrate and verify our method. Finally, we extend our method for selection with preference variability to the case where the attribute levels of design alternatives also have variability. We assume that the variability in attribute levels can be quantified with a range of attribute levels.

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Copyright © 2006 by American Society of Mechanical Engineers
Topics: Design
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References

Figures

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Figure 1

Illustration of gradient cut

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Figure 2

Flow chart of our method for selection with preference variability

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Figure 3

Illustration of approach for eliminating dominated designs based on range of MRS preferences

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Figure 4

Illustration of heuristic approach for eliminating dominated designs

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Figure 5

Flow chart of our method for selection with preference and attribute variability

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Figure 6

Illustration of approach for eliminating dominated designs based on the range of MRS preferences and the range of attribute levels of design alternatives

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Figure 7

Dominated designs when β lies between 11 and 18 at DT1

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Figure 8

Value of payload design alternatives for different β’s

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