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Optimization of multiple responses in the Taguchi method using fuzzy regression

Published online by Cambridge University Press:  20 January 2014

Abbas Al-Refaie
Abbas Al-Refaie*
Affiliation:
Department of Industrial Engineering, University of Jordan, Amman, Jordan
*
Reprint requests to: Abbas Al-Refaie, Department of Industrial Engineering, University of Jordan, Amman 11942, Jordan. E-mail: abbas.alrefai@ju.edu.jo
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Abstract

In reality, the behavior of processes is sometimes vague and the observed data is irregular. This research proposes an approach for optimizing fuzzy multiple responses using fuzzy regression. In this approach, each response repetition is transformed into a signal to noise ratio then modeled using statistical multiple regression. A trapezoidal fuzzy regression model is formulated for each response utilizing the statistical regression coefficients. The most desirable response values and the deviation function are determined for each response. Finally, four optimization models are formulated for the trapezoidal membership fuzzy number to obtain the optimal factor level at each number. Two case studies are adopted for illustration, where excluding response fuzziness will result in misleading optimal factor settings if solved by the traditional optimization techniques. In conclusion, the proposed approach based on fuzzy regression approach can successfully optimize fuzzy multiple responses in a wide range of manufacturing applications on the Taguchi's method. Moreover, compared to other approaches, such as data envelopment analysis and grey relational analysis, the proposed approach has the distinct advantage of being able to generate models using only a small number of experimental data sets and minimizing inherent variations.

Keywords

Desirability FunctionFuzzy RegressionOptimizationTaguchi Method
Type
Technical Brief
Information
AI EDAM , Volume 28 , Issue 1 , February 2014 , pp. 99 - 107
Copyright
Copyright © Cambridge University Press 2014 

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