References
- Apley DW, Kim JA. A cautious approach to robust design with model parameter uncertainty. IIE Trans. 2011;43:471–482.
- Ouyang LH, Ma YZ, Wang JJ, et al. A new loss function for multi-response optimization with model parameter uncertainty and implementation errors. Eur J Oper Res. 2017;258:552–563.
- Peterson JJ. A posterior predictive approach to multiple response surface optimization. J Qual Technol. 2004;36:139–153.
- Ng SH. A Bayesian-model averaging approach for multiple-response problem. J Qual Technol. 2010;42:52–68.
- Vining GG, Myers R. Combining Taguchi and response surface philosophies – a dual response approach. J Qual Technol. 1990;22:38–45.
- Vining GG, Bohn LL. Response surfaces for the mean and variance using a nonparametric approach. J Qual Technol. 1998;30:282–291.
- Pickle SM, Robinson TJ, Birch JB, et al. A semi-parametric approach to robust parameter design. J Stat Plan Inference. 2008;138:114–131.
- Robinson TJ, Birch JB, Starnes BA. A semi-parametric approach to dual modeling when no replication exists. J Stat Plan Inference. 2010;140:2860–2869.
- Wan W, Birch JB. A semiparametric technique for the multi-response optimization problem. Qual Reliability Eng Int. 2010;27:47–59.
- He Z, Zhu PF, Park SH. A robust desirability function method for multi-response surface optimization considering model uncertainty. Eur J Oper Res. 2012;221:241–247.
- Ouyang LH, Ma YZ, Byun JH. An integrative loss function approach to multi-response optimization. Qual Reliability Eng Int. 2015;31:193–204.
- Wang JJ, Ma YZ, Ouyang LH, et al. A new Bayesian approach to multi-response surface optimization integrating loss function with posterior probability. Eur J Oper Res. 2016;249:231–237.
- Jin R, Deng X. Ensemble modeling for data fusion in manufacturing process scale-up. IIE Trans. 2015;47:203–214.
- Rajagopal R, Del Castillo E. Model-robust process optimization using Bayesian model averaging. Technometrics. 2005;47:152–163.
- Zhou XJ, Ma YZ, Tu YL, et al. Ensemble of surrogates for dual response surface modeling in robust parameter design. Qual Reliability Eng Int. 2013;29:173–197.
- Hamza K, Saitou K. A co-evolutionary approach for design optimization via ensemble of surrogates with application to vehicle crashworthiness. J Mechanical Des. 2012;134:1–10.
- Mays JE, Birch JB, Einsporn RL. An overview of model-robust regression. J Stat Comput Simul. 2000;66:79–100.
- Lee KH, Kang DH. A robust optimization using the statistics based on Kriging metamodel. J Mechanical Sci Technol. 2006;20:1169–1182.
- Jin R, Chen W, Simpson TW. Comparative studies of metamodelling techniques under multiple modelling criteria. Structural Multidiscipline Optimization. 2001;23:1–13.
- Sack J, Schiller SB, Welch WJ. Designs for computer experiments. Technometrics. 1989;31:41–47.
- Meckesheimer M, Barton RR, Simpson TW. Computationally inexpensive metamodel assessment strategies. Aiaa J. 2011;40:2053–2060.
- McDonald DB, Grantham WJ, Tabor WL, et al. Global and local optimization using radial basis function response surface models. Appl Math Model. 2007;31:2095–2110.
- Acar E, Rais-Rohani M. Ensemble of metamodels with optimized weight factors. Structural Multidiscipline Optimization. 2009;37:279–294.
- Goel T, Haftka RT, Shyy W, et al. Ensemble of surrogates. Structural Multidiscipline Optimization. 2007;33:199–216.
- Sanchez E, Pintos S, Queipo N. Toward an optimal ensemble of kernel-based approximations with engineering application. Structural Multidiscipline Optimization. 2008;36:247–261.
- Montgomery DC. Design and analysis of experiment. NY: Wiley; 2008.