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Research Papers

Optimal Experimental Design of Human Appraisals for Modeling Consumer Preferences in Engineering Design

[+] Author and Article Information
Christopher Hoyle

 Northwestern University, 2145 Sheridan Rd., Evanston, IL 60208–3111cj-hoyle@u.northwestern.edu

Wei Chen

 Northwestern University, 2145 Sheridan Rd., Evanston, IL 60208–3111weichen@northwestern.edu

Bruce Ankenman

 Northwestern University, 2145 Sheridan Rd., Evanston, IL 60208–3111ankenman@iems.northwestern.edu

Nanxin Wang

 Ford Research and Advanced Engineering, 2101 Village Road, Dearborn, MI 48124ankenman@iems.northwestern.edu

J. Mech. Des 131(7), 071008 (Jun 24, 2009) (9 pages) doi:10.1115/1.3149845 History: Received April 11, 2008; Revised March 26, 2009; Published June 24, 2009

Human appraisals are becoming increasingly important in the design of engineering systems to link engineering design attributes to customer preferences. Human appraisals are used to assess consumers’ opinions of a given product design, and are unique in that the experiment response is a function of both the product attributes and the respondents’ human attributes. The design of a human appraisal is characterized as a split-plot design, in which the respondents’ human attributes form the whole-plot factors while the product attributes form the split-plot factors. The experiments are also characterized by random block effects, in which the design configurations evaluated by a single respondent form a block. An experimental design algorithm is needed for human appraisal experiments because standard experimental designs often do not meet the needs of these experiments. In this work, an algorithmic approach to identify the optimal design for a human appraisal experiment is developed, which considers the effects of respondent fatigue and the blocked and split-plot structures of such a design. The developed algorithm seeks to identify the experimental design, which maximizes the determinant of the Fisher information matrix. The algorithm is derived assuming an ordered logit model will be used to model the rating responses. The advantages of this approach over competing approaches for minimizing the number of appraisal experiments and model-building efficiency are demonstrated using an automotive interior package human appraisal as an example.

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Copyright © 2009 by American Society of Mechanical Engineers
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References

Figures

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Figure 3

Algorithmic implementation of the optimal experimental design method

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PVM used for conducting human appraisal experiments (8)

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Occupant package blocked split-plot human appraisal experiment

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The structure of blocked and split-plot experiments (28)

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The structure of the human appraisal blocked split-plot experiment (28)

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Figure 6

Comparison of rating predictions to actual ratings

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