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An augmented approach to the desirability function

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  • Hsiu-Wen Chen
  • Weng Kee Wong
  • Hongquan Xu

Abstract

The desirability function is widely used in the engineering field to tackle the problem of optimizing multiple responses simultaneously. This approach does not account for the variability in the predicted responses and minimizing this variability to have narrower prediction intervals is desirable. We propose to add this capability in the desirability function and also incorporate the relative importance of optimizing the multiple responses and minimizing the variances of the predicted responses at the same time. We show that the benefits of our augmented approach using two real data sets by comparing our solutions with those obtained from the desirability approach. In particular, it is shown that our approach offers greater flexibility and the solutions can reduce the variances of all the predicted responses resulting in narrower prediction intervals.

Suggested Citation

  • Hsiu-Wen Chen & Weng Kee Wong & Hongquan Xu, 2012. "An augmented approach to the desirability function," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(3), pages 599-613, July.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:599-613
    DOI: 10.1080/02664763.2011.605437
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    References listed on IDEAS

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    1. Kim, Kwang-Jae & Lin, Dennis K.J., 2006. "Optimization of multiple responses considering both location and dispersion effects," European Journal of Operational Research, Elsevier, vol. 169(1), pages 133-145, February.
    2. Kwang‐Jae Kim & Dennis K. J. Lin, 2000. "Simultaneous optimization of mechanical properties of steel by maximizing exponential desirability functions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(3), pages 311-325.
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