How to cite this paper
Gauri, S & Pal, S. (2017). Optimization of multi-response dynamic systems using multiple regression-based weighted signal-to-noise ratio.International Journal of Industrial Engineering Computations , 8(1), 161-178.
Refrences
Bae, S. J., & Tsui, K. L. (2006). Analysis of dynamic robust design experiment with explicit & hidden noise variables. Quality Technology & Quantitative Management, 3(1), 55-75.
Box, G. (1988). Signal-to-noise ratios, performance criteria, and transformations. Technometrics, 30(1), 1-17.
Chang, H. H. (2006). Dynamic multi-response experiments by backpropagation networks and desirability functions. Journal of the Chinese Institute of Industrial Engineers, 23(4), 280-288.
Chang, H. H. (2008). A data mining approach to dynamic multiple responses in Taguchi experimental design. Expert Systems with Applications, 35(3), 1095-1103.
Chang, H. H., & Chen, Y. K. (2011). Neuro-genetic approach to optimize parameter design of dynamic multiresponse experiments. Applied Soft Computing, 11(1), 436-442.
Chen, S. P. (2003). Robust design with dynamic characteristics using stochastic sequential quadratic programming. Engineering Optimization, 35(1), 79-89.
Del Castillo, E., & Montgomery, D. C. (1993). A nonlinear programming solution to the dual response problem. Journal of Quality Technology, 25(3), 199-204.
Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.
Gauri, S. K. (2014). Optimization of multi-response dynamic systems using principal component analysis (PCA)-based utility theory approach. International Journal of Industrial Engineering Computations, 5(1), 101-114.
Harrington, E. C. (1965). The desirability function. Industrial quality control, 21(10), 494-498.
Hsieh, K. L., Tong, L. I., Chiu, H. P., & Yeh, H. Y. (2005). Optimization of a multiresponse problem in Taguchi's dynamic system. Computers & Industrial Engineering, 49(4), 556-571.
Jeong, I. J., & Kim, K. J. (2009). An interactive desirability function method to multi-response optimization. European Journal of Operational Research, 195(2), 412-426.
Joseph, V.R., & Wu, C.F.J. (2002). Robust parameter design of multiple-target systems. Technometrics, 44(4), 338-346.
Khuri, A. I., & Conlon, M. (1981). Simultaneous optimization of multiple responses represented by polynomial regression functions. Technometrics, 23(4), 363–375.
León, R. V., Shoemaker, A. C., & Kacker, R. N. (1987). Performance measures independent of adjustment: an explanation and extension of Taguchi's signal-to-noise ratios. Technometrics, 29(3), 253-265.
Lesperance, M. L., & Sung-Min, P. (2003). GLMs for the analysis of robust designs with dynamic characteristics. Journal of Quality Technology, 35(3), 253-263.
Liao, H. C. (2006). Multi-response optimization using weighted principal component. The International Journal of Advanced Manufacturing Technology, 27(7-8), 720-725.
Lin, D. K., & Tu, W. (1995). Dual response surface optimization. Journal of Quality Technology, 27(1), 34-39.
McCaskey, S. D., & Tsui, K. L. (1997). Analysis of dynamic robust design experiments. International Journal of Production Research, 35(6), 1561-1574.
Miller, A., & Wu, C. F. J. (1996). Parameter design for signal-response systems: A different look at Taguchi's dynamic parameter design. Statistical Science, 11(2), 122-136.
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2015). Introduction to linear regression analysis. New York: John Wiley & Sons.
Pal, S., & Gauri, S. K. (2010). Multi-Response Optimization Using Multiple Regression–Based Weighted Signal-to-Noise Ratio (MRWSN). Quality Engineering, 22(4), 336-350.
Pignatiello, Jr., & Joseph, J. (1993). Strategies for robust multi-response quality engineering. IIE Transactions, 25(3), 5-15.
Su, C. T., Chen, M. C., & Chan, H. L. (2005). Applying neural network and scatter search to optimize parameter design with dynamic characteristics. Journal of the Operational Research Society, 56(10), 1132-1140.
Su, C. T., & Tong, L. I. (1997). Multi-response robust design by principal component analysis. Total Quality Management, 8(6), 409–416.
Taguchi, G. (1986). Introduction to quality engineering: designing quality into products and processes. Tokyo, Japan: Asian Productivity Organization.
Tong, L. I., Chen, C. C., & Wang, C. H. (2007). Optimization of multi-response processes using the VIKOR method. The International Journal of Advanced Manufacturing Technology, 31(11-12), 1049-1057.
Tong, L.I., & Hsieh, K.L. (2000). A novel means of applying artificial neural networks to optimize multi-response problem. Quality Engineering, 13(1), 11–18.
Tong, L. I., Wang, C. H., Houng, J. Y., & Chen, J. Y. (2001). Optimizing dynamic multi-response problems using the dual-response-surface method. Quality Engineering, 14(1), 115-125.
Tong, L. I., Wang, C. H., Chen, C. C., & Chen, C. T. (2004). Dynamic multiple responses by ideal solution analysis. European Journal of Operational Research, 156(2), 433-444.
Tong, L. I., Wang, C. H., & Tsai, C. W. (2008). Robust design for multiple dynamic quality characteristics using data envelopment analysis. Quality and Reliability Engineering International, 24(5), 557-571.
Tsui, K. L. (1999). Modeling and analysis of dynamic robust design experiments. IIE Transactions, 31(12), 1113-1122.
Tsui, K. (2001). Response model analysis of dynamic robust design experiments, in: Lenz, H.J., Wilrich, P.T. (Eds.) Frontiers in Statistical Quality Control 6. Physica-Verlag Heidelberg, New York, pp. 360-370.
Wang, C. H., & Tong, L. I. (2004). Optimization of dynamic multi-response problems using grey multiple attribute decision making. Quality Engineering, 17(1), 1-9.
Wang, C. H. (2007). Dynamic multi-response optimization using principal component analysis and multiple criteria evaluation of the grey relation model. The International Journal of Advanced Manufacturing Technology, 32(5-6), 617-624.
Wang, J., Ma, Y., Ouyang, L., & Tu, Y. (2016). A new Bayesian approach to multi-response surface optimization integrating loss function with posterior probability. European Journal of Operational Research, 249(1), 231-237.
Wasserman, G. S. (1996). Parameter design with dynamic characteristics: A regression perspective. Quality and Reliability Engineering International, 12(2), 113-117.
Wu, C. F. J. (2004). Optimization of correlated multiple quality characteristics using desirability function. Quality Engineering, 17(1), 119-126.
Wu, C.F.J., & Hamada, M. (2000). Experiments: Planning, analysis, and parameter design optimization. New York: Wiley-Interscience.
Wu, F. C., & Yeh, C. H. (2005). Robust design of multiple dynamic quality characteristics. The International Journal of Advanced Manufacturing Technology, 25(5-6), 579-588.
Wu, F. C. (2009). Robust design of nonlinear multiple dynamic quality characteristics. Computers & Industrial Engineering, 56(4), 1328-1332.
Box, G. (1988). Signal-to-noise ratios, performance criteria, and transformations. Technometrics, 30(1), 1-17.
Chang, H. H. (2006). Dynamic multi-response experiments by backpropagation networks and desirability functions. Journal of the Chinese Institute of Industrial Engineers, 23(4), 280-288.
Chang, H. H. (2008). A data mining approach to dynamic multiple responses in Taguchi experimental design. Expert Systems with Applications, 35(3), 1095-1103.
Chang, H. H., & Chen, Y. K. (2011). Neuro-genetic approach to optimize parameter design of dynamic multiresponse experiments. Applied Soft Computing, 11(1), 436-442.
Chen, S. P. (2003). Robust design with dynamic characteristics using stochastic sequential quadratic programming. Engineering Optimization, 35(1), 79-89.
Del Castillo, E., & Montgomery, D. C. (1993). A nonlinear programming solution to the dual response problem. Journal of Quality Technology, 25(3), 199-204.
Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.
Gauri, S. K. (2014). Optimization of multi-response dynamic systems using principal component analysis (PCA)-based utility theory approach. International Journal of Industrial Engineering Computations, 5(1), 101-114.
Harrington, E. C. (1965). The desirability function. Industrial quality control, 21(10), 494-498.
Hsieh, K. L., Tong, L. I., Chiu, H. P., & Yeh, H. Y. (2005). Optimization of a multiresponse problem in Taguchi's dynamic system. Computers & Industrial Engineering, 49(4), 556-571.
Jeong, I. J., & Kim, K. J. (2009). An interactive desirability function method to multi-response optimization. European Journal of Operational Research, 195(2), 412-426.
Joseph, V.R., & Wu, C.F.J. (2002). Robust parameter design of multiple-target systems. Technometrics, 44(4), 338-346.
Khuri, A. I., & Conlon, M. (1981). Simultaneous optimization of multiple responses represented by polynomial regression functions. Technometrics, 23(4), 363–375.
León, R. V., Shoemaker, A. C., & Kacker, R. N. (1987). Performance measures independent of adjustment: an explanation and extension of Taguchi's signal-to-noise ratios. Technometrics, 29(3), 253-265.
Lesperance, M. L., & Sung-Min, P. (2003). GLMs for the analysis of robust designs with dynamic characteristics. Journal of Quality Technology, 35(3), 253-263.
Liao, H. C. (2006). Multi-response optimization using weighted principal component. The International Journal of Advanced Manufacturing Technology, 27(7-8), 720-725.
Lin, D. K., & Tu, W. (1995). Dual response surface optimization. Journal of Quality Technology, 27(1), 34-39.
McCaskey, S. D., & Tsui, K. L. (1997). Analysis of dynamic robust design experiments. International Journal of Production Research, 35(6), 1561-1574.
Miller, A., & Wu, C. F. J. (1996). Parameter design for signal-response systems: A different look at Taguchi's dynamic parameter design. Statistical Science, 11(2), 122-136.
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2015). Introduction to linear regression analysis. New York: John Wiley & Sons.
Pal, S., & Gauri, S. K. (2010). Multi-Response Optimization Using Multiple Regression–Based Weighted Signal-to-Noise Ratio (MRWSN). Quality Engineering, 22(4), 336-350.
Pignatiello, Jr., & Joseph, J. (1993). Strategies for robust multi-response quality engineering. IIE Transactions, 25(3), 5-15.
Su, C. T., Chen, M. C., & Chan, H. L. (2005). Applying neural network and scatter search to optimize parameter design with dynamic characteristics. Journal of the Operational Research Society, 56(10), 1132-1140.
Su, C. T., & Tong, L. I. (1997). Multi-response robust design by principal component analysis. Total Quality Management, 8(6), 409–416.
Taguchi, G. (1986). Introduction to quality engineering: designing quality into products and processes. Tokyo, Japan: Asian Productivity Organization.
Tong, L. I., Chen, C. C., & Wang, C. H. (2007). Optimization of multi-response processes using the VIKOR method. The International Journal of Advanced Manufacturing Technology, 31(11-12), 1049-1057.
Tong, L.I., & Hsieh, K.L. (2000). A novel means of applying artificial neural networks to optimize multi-response problem. Quality Engineering, 13(1), 11–18.
Tong, L. I., Wang, C. H., Houng, J. Y., & Chen, J. Y. (2001). Optimizing dynamic multi-response problems using the dual-response-surface method. Quality Engineering, 14(1), 115-125.
Tong, L. I., Wang, C. H., Chen, C. C., & Chen, C. T. (2004). Dynamic multiple responses by ideal solution analysis. European Journal of Operational Research, 156(2), 433-444.
Tong, L. I., Wang, C. H., & Tsai, C. W. (2008). Robust design for multiple dynamic quality characteristics using data envelopment analysis. Quality and Reliability Engineering International, 24(5), 557-571.
Tsui, K. L. (1999). Modeling and analysis of dynamic robust design experiments. IIE Transactions, 31(12), 1113-1122.
Tsui, K. (2001). Response model analysis of dynamic robust design experiments, in: Lenz, H.J., Wilrich, P.T. (Eds.) Frontiers in Statistical Quality Control 6. Physica-Verlag Heidelberg, New York, pp. 360-370.
Wang, C. H., & Tong, L. I. (2004). Optimization of dynamic multi-response problems using grey multiple attribute decision making. Quality Engineering, 17(1), 1-9.
Wang, C. H. (2007). Dynamic multi-response optimization using principal component analysis and multiple criteria evaluation of the grey relation model. The International Journal of Advanced Manufacturing Technology, 32(5-6), 617-624.
Wang, J., Ma, Y., Ouyang, L., & Tu, Y. (2016). A new Bayesian approach to multi-response surface optimization integrating loss function with posterior probability. European Journal of Operational Research, 249(1), 231-237.
Wasserman, G. S. (1996). Parameter design with dynamic characteristics: A regression perspective. Quality and Reliability Engineering International, 12(2), 113-117.
Wu, C. F. J. (2004). Optimization of correlated multiple quality characteristics using desirability function. Quality Engineering, 17(1), 119-126.
Wu, C.F.J., & Hamada, M. (2000). Experiments: Planning, analysis, and parameter design optimization. New York: Wiley-Interscience.
Wu, F. C., & Yeh, C. H. (2005). Robust design of multiple dynamic quality characteristics. The International Journal of Advanced Manufacturing Technology, 25(5-6), 579-588.
Wu, F. C. (2009). Robust design of nonlinear multiple dynamic quality characteristics. Computers & Industrial Engineering, 56(4), 1328-1332.