The use of nested desirability functions and quality indices for multi-response robust parameter design problems

References

• Ames, A. E., N. S. MacDonald, G. Szonyi, and D. M. Hawkins. 1997. Quality loss functions for optimization across multiple response surfaces. Journal of Quality Technology 29 (3):339–346.
   Google Scholar
• Antony, J. 2000. Multi-response optimization in industrial experiments using Taguchi's quality loss function and principal component analysis. Quality and Reliability Engineering International 16 (1):3–8.
   Google Scholar
• Auer, C., M. Erdbrugge, and R. Gobel. 2004. Comparison of multivariate methods for robust parameter design in sheet metal. Applied Stochastic Models in Business and Industry 20 (3):201–218.
   Google Scholar
• Bellucci, J. P. 2016. Non-linear metamodeling extensions to the robust parameter design of computer simulations. Ph.D. diss., Air Force Institute of Technology.
   Google Scholar
• Bellucci, J. P., and K. W. Bauer. 2016. A Taylor series approach to the robust parameter design of computer simulations using Kriging and radial basis function neural networks. International Journal of Quality Engineering and Technology, in review.
   Google Scholar
• Chiao, C. H., and M. Hamada. 2001. Analyzing experiments with correlated multiple responses. Journal of Quality Technology 33 (4):451–465.
   Google Scholar
• Del Castillo, E., D. C. Montgomery, and D. R. McCarville. 1996. Modified desirability functions for multiple response optimization. Journal of Quality Technology 28 (3):337–345.
   Google Scholar
• Derringer, G., and D. Suich. 1980. Simultaneous optimization of several response variables. Journal of Quality Technology 12 (4):214–219.
   Google Scholar
• Gomes, J. H. F., A. P. Paiva, S. C. Costa, P. P. Balestrassi, and E. J. Paiva. 2013. Weighted multivariate mean square error for process optimization: a case study of flux-cored arc welding for stainless steel claddings. European Journal of Operational Research 226 (3):522–535.
   Google Scholar
• Govindaluri, S. M., and B. R. Cho. 2007. Robust design modeling with correlated quality characteristics using a multicriteria decision framework. International Journal of Advanced Manufacturing Technology 32 (5–6):423–433.
   Google Scholar
• Harrington, E. C. 1965. The desirability function. Industrial Quality Control 21 (10):494–498.
   Google Scholar
• Hwang, I. 1996. Strategies for multiple response robust quality engineering using desirability functions. Ph.D. diss., Texas A&M University.
   Google Scholar
• Idzorek, T. M. 2007. A step-by-step guide to the Black-Litterman model. In Forecasting expected returns in financial markets, ed. S. Satchell, 17–38. London: Academic Press.
   Google Scholar
• Kelton, W. D., R. P. Sadowski, and D. T. Sturrock. 2004. Simulation with arena, 3rd ed., New York: McGraw-Hill.
   Google Scholar
• Khuri, A. I., and M. Conlon. 1981. Simultaneous optimization of multiple responses represented by polynomial regression functions. Technometrics 23 (4):363–375.
   Google Scholar
• Ko, Y. H., K. J., Kim, and C. H. Jun. 2005. A new loss function-based method for multiresponse optimization. Journal of Quality Technology 37 (1):50–59.
   Google Scholar
• Koksoy, O. 2006. Multiresponse robust design: mean square error (MSE) criterion. Applied Mathematics and Computation 175 (2):1716–1729.
   Google Scholar
• Koksoy, O. 2008. A nonlinear programming solution to robust multi-response quality problems. Applied Mathematics and Computation 196 (2):603–612.
   Google Scholar
• Koksoy, O., and T. Yalcinoz. 2006. Mean square error criteria to multiresponse process optimization by a new genetic algorithm. Applied Mathematics and Computation 175 (2):1657–1674.
   Google Scholar
• Kwon, Y. M., and J. J. Lee. 2013. Robust design using desirability function to the combined-array with multiple quality characteristics. Journal of the Chosun Natural Science 6 (1):39–45.
   Google Scholar
• Liao, H. C. 2006. Multi-response optimization using weighted principal component. The International Journal of Advanced Manufacturing 27 (7–8):720–725.
   Google Scholar
• Lin, D. K. J., and W. Tu. 1995. Dual response surface optimization. Journal of Quality Technology 27 (1):34–39.
   Google Scholar
• Mindrup, F. M., K. W. Bauer, and M. A. Friend. 2012. Extending robust parameter design to noise by noise interactions with an application to hyperspectral imagery. International Journal of Quality Engineering and Technology 3 (1):1–19.
   Google Scholar
• Myers, R. H., A. I. Khuri, and G. Vining. 1992. Response surface alternatives to the Taguchi robust rarameter design approach. The American Statistician 46 (2):131–139.
   Google Scholar
• Najafi, S., A. Salmasnia, and R. B. Kazemzadeh. 2011. Optimization of robust design for multiple response problem. Australian Journal of Basic and Applied Sciences 5 (9):1566–1577.
   Google Scholar
• Ozturk, B. A., G. Koksal, and G. W. Weber. 2010. Optimization of desirability functions as a DNLP model by GAMS/BARON. POWER CONTROL AND OPTIMIZATION: Proceedings of the 3rd Global Conference on Power Control and Optimization 1239 (1):305–310.
   Google Scholar
• Paiva, A. P., P. H. Campos, J. R. Ferriera, L. G. D. Lopes, E. J. Paiva, and P. P. Balestrassi. 2012. A multivariate robust parameter design approach for optimization of AISI 52100 hardened steel turning with wiper mixed ceramic tool. International Journal of Refractory Metals and Hard Materials 30 (1):152–163.
   Google Scholar
• Park, S. H. 1981. Simultaneous optimization techniques for multi-purpose response functions. Journal of Military Operations Research Society of Korea 7:118–138.
   Google Scholar
• Park, S. H., and J. O. Park. 1998. Simultaneous optimization of multiple responses using a weighted desirability function. In Quality improvement through statistical methods, 299–311, Boston, MA: Birkhäuser.
   Google Scholar
• Pignatiello, J. J. 1993. Strategies for robust multiresponse quality engineering. IIE Transactions 25 (3):5–15.
   Google Scholar
• Romano, D., M. Varetto, and G. Vicario. 2004. Multiresponse robust design: a general framework based on combined array. Journal of Quality Technology 36 (1):27–37.
   Google Scholar
• Routara, B. C., S. D. Mohanty, D. Saurav, A. Bandyopadhyay, and S. S. Mahapatra. 2010. Combined quality loss (CQL) concept in WPCA-based Taguchi philosophy for optimization of multiple surface quality characteristics in UNS C34000 brass in cylindrical grinding. The International Journal of Advanced Manufacturing Technology 51 (1–4):135–143.
   Google Scholar
• Salmasnia, A., R. B. Kazemzadeh, and S. T. A. Niaki. 2012. An approach to optimize correlated multiple responses using principal component analysis and desirability function. International Journal of Advanced Manufacturing Technology 62 (5):835–846.
   Google Scholar
• Schöttle, K., and R. Werner. 2006. Consistency of robust portfolio estimators. OR and Management Sciences 1–23.
   Google Scholar
• Shengnan, Z., and W. Jianjun. 2015. Multi-response robust design based on improved desirability function. Paper presented at the 2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS), Leicester, United Kingdom, August 18–20.
   Google Scholar
• Su, C. T., and L. I. Tong. 1997. Multi-response robust design by principal component analysis. Total Quality Management 8 (6):409–416.
   Google Scholar
• Taguchi, G. 1986. Introduction to quality engineering: Designing quality into products and processes. White Plains, NY: UNIPUB/Kraus International.
   Google Scholar
• Vining, G. G. 1998. A compromise approach to multiresponse optimization. Journal of Quality Technology 30 (4):309–313.
   Google Scholar
• Wang, J., Z. He, J. H. Oh, and S. H. Park. 2008. Multi-response robust optimization using desirability function. Paper presented at the 2008 IEEE Symposium on Advanced Management of Information for Globalized Enterprises (AMIGE 2008), Tianjin, China, September 28–29.
   Google Scholar
• Werner, R. 2010. Consistency of robust optimization with application to portfolio optimization. Optimization (Online). http://www.optimization-online.org/DB_FILE/2010/09/2747.pdf (accessed March 22, 2017).
   Google Scholar
• Wild, R. H., and J. J. Pignatiello. 1991. An experimental design strategy for designing robust systems using discrete-event simulation. Simulation 57 (6):358–368.
   Google Scholar
• Williams, J. P., K. W. Bauer, and M. A. Friend. 2013. Further extensions to robust parameter design: three factor interactions with an application to hyperspectral imagery. International Journal of Quality Engineering and Technology 3 (3):204–218.
   Google Scholar