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A Fuzzy Multiple Regression Approach for Optimizing Multiple Responses in the Taguchi Method

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  • Abbas Al-Refaie

    (University of Jordan, Jordan)

  • Ibrahim Rawabdeh

    (University of Jordan, Jordan)

  • Reema Abu-alhaj

    (University of Jordan, Jordan)

  • Issam S. Jalham

    (University of Jordan, Jordan)

Abstract

The fuzzy regression has been found effective in modeling the relationship between the dependent variable and independent variables when a high degree of fuzziness is involved and only a few data sets are available for model building. This research, therefore, proposes an approach for optimizing multiple responses in the Taguchi method using fuzzy regression and desirability function. The statistical regression is formulated for the signal to noise (S/N) ratios of each response replicate. Then, the optimal factor levels for each replicate are utilized in building fuzzy regression model. The desirability function, pay-off matrix, and the deviation function are finally used for formulating the optimization models for the lower, mean, and upper limits. Two case studies investigated in previous literature are employed for illustration; where in both case studies the proposed approach efficiently optimized processes performance.

Suggested Citation

  • Abbas Al-Refaie & Ibrahim Rawabdeh & Reema Abu-alhaj & Issam S. Jalham, 2012. "A Fuzzy Multiple Regression Approach for Optimizing Multiple Responses in the Taguchi Method," International Journal of Fuzzy System Applications (IJFSA), IGI Global, vol. 2(3), pages 13-34, July.
    • Handle: RePEc:igg:jfsa00:v:2:y:2012:i:3:p:13-34
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    Cited by:

    1. Abbas Al-Refaie & Wafa’a Al-Alaween & Ali Diabat & Ming-Hsien Li, 2017. "Solving dynamic systems with multi-responses by integrating desirability function and data envelopment analysis," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 387-403, February.
    2. Abbas Al-Refaie & Rami Fouad & Raed Athamneh, 2019. "Fuzzy Logic and Back-Propagation Neural Networks for Optimal Performance," Modern Applied Science, Canadian Center of Science and Education, vol. 13(2), pages 157-157, February.

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