A robust posterior preference multi-response optimization approach in multistage processes

References

• Antony, J. 2001. Simultaneous optimization of multiple characteristics in manufacturing processes using Taguchi's quality loss function. International Journal of Advanced Manufacturing Technology 17:134–138.
   Web of Science ®Google Scholar
• Baril, C., S. Yacout, and B. Clément. 2011. Design for six sigma through collaborative multi-objective optimization. Computers and Industrial Engineering 60:43–55.
   Web of Science ®Google Scholar
• Bashiri, M., and A. Moslemi. 2013a. The analysis of residuals variation and outliers to obtain robust response surface. Journal of Industrial Engineering International 9:1–10.
   Google Scholar
• Bashiri, M., and A. Moslemi. 2013b. Simultaneous robust estimation of multi-response surfaces in the presence of outliers. Journal of Industrial Engineering International 9:1–12.
   Google Scholar
• Chang, S. 1997. An algorithm to generate near D-optimal designs for multiple response surface models. IIE Transactions 29:1073–1081.
   Web of Science ®Google Scholar
• Chiao, C. H., and M. Hamada. 2001. Analyzing experiments with correlated multiple responses. Journal of Quality Technology 33:451–465.
   Web of Science ®Google Scholar
• Costa, N. R., J. Lourenço, and Z. L. Pereira. 2011. Multiresponse optimization and Pareto frontiers. Quality and Reliability Engineering International 28:701–712.
   Web of Science ®Google Scholar
• Diaz-Garcia, J. A., R. Ramos-Quiroga, and E. Cabrera-Vicencio. 2005. Stochastic programming methods in the response surface methodology. Computational Statistics & Data Analysis 49:837–848.
   Web of Science ®Google Scholar
• Del Castillo, E., B. M. Colosimo, and H. Alshraideh. 2012. Bayesian Modeling and Optimization of Functional Responses Affected by Noise Factors, Journal of Quality Technology 44:117–135.
   Web of Science ®Google Scholar
• Edwards, D. J., and J. N. Fuerte. 2011. Compromise ascent directions for multiple-response applications. Quality and Reliability Engineering International 27:1107–1118.
   Web of Science ®Google Scholar
• Figueira, J., S. Greco, and M. Ehrgott. 2005. Multiple criteria decision analysis: state of the art surveys. New York: Springer, p. 3–17.
   Google Scholar
• Giri, N. C. 1977. Multivariate statistical inference. Walt ham: Academic Press, p. 117–159.
   Google Scholar
• Haimes, Y. Y., L. S. Lasdon, and D. A. Wismer. 1971. On a bicriterion formulation of the problems of integrated system identification and system optimization. IEEE Transactions on Systems Man, and Cybernetics 1:296–297.
   Google Scholar
• He, Z., P. Zhou, M. Zhang, and T. N. Goh. 2015. A review of analysis of dynamic response in design of experiments. Quality and Reliability Engineering International 31:535–542.
   Web of Science ®Google Scholar
• Hejazi, T. H., M. Bashiri, K. Noghondarian, and A. C. Atkinson. 2011. Multiresponse optimization with consideration of probabilistic covariates. Quality and Reliability Engineering International 27:437–449.
   Web of Science ®Google Scholar
• Hejazi, T. H., M. Seyyed-Esfahani, and M. Mahootchi. 2013. Quality chain design and optimization by multiple response surface methodology. International Journal of Advanced Manufacturing Technology 68:881–893.
   Web of Science ®Google Scholar
• Hwang, C. L., A. S. M. Masud, S. R. Paidy, and K. Yoon 1979. Multiple objective decision making - methods and applications: a state of the art survey. Berlin: Springer-Verlag, p. 78–93.
   Google Scholar
• Johnson, R., and D. Wichern. 1982. Applied multivariate statistical analysis. New York: John Wiley & Sons, p. 147–182.
   Google Scholar
• Kandel, R. 1991, Our Changing Climate. New York: McGraw-Hill, p. 78–92.
   Google Scholar
• Kazemzadeh, R. B., M. Bashiri, A. C. Atkinson, and R. Noorossana. 2008. A general framework for multiresponse optimization problems based on goal programming. European Journal of Operational Research 189:421–429.
   Web of Science ®Google Scholar
• Khuri, A. I., and M. Conlon. 1981. Simultaneous optimization of multiple responses represented by polynomial regression functions. Technometrics 23:363–375.
   Web of Science ®Google Scholar
• Khuri, A. I. 1996. Multiresponse surface methodology. In Handbook of Statistics: Design and Analysis of Experiment, ed. A. Ghosh and C. R. Rao, Amsterdam, Netherlands, 377–406.
   Google Scholar
• Kim, P., and Y. Ding. 2004. Optimal design of fixture layout in multistation assembly processes. IEEE Transactions on Automation Science and Engineering 1:133–145.
   Web of Science ®Google Scholar
• Koksoy, O. 2008. A nonlinear programming solution to robust multiresponse quality problem. Applied Mathematics and Computation 196: 603–612.
   Web of Science ®Google Scholar
• Koksoy, O., and T. Yalcionoz. 2006. Mean square error criteria to multiresponse process optimization by a new genetic algorithm. Applied Mathematics and Computation 175:1675–1674.
   Web of Science ®Google Scholar
• Kovach, J., and B. R. Cho. 2008. A D-optimal design approach to constrained multiresponse robust design with prioritized mean and variance considerations. Computers and Industrial Engineering 57: 237–245.
   Web of Science ®Google Scholar
• Kovach, J., and B. R. Cho. 2009. A D-optimal design approach to constrained multiresponse robust design with prioritized mean and variance considerations. Computers and Industrial Engineering 57:237–245.
   Web of Science ®Google Scholar
• Lee, D., I. Jeong, and K. Kim. 2010. A posterior preference articulation approach to dual response surface optimization. IIE Transactions 42:161–171.
   Web of Science ®Google Scholar
• Lee, D., K. Kim, and M. Köksalan. 2011. A posterior preference articulation approach to multi response surface optimization. European Journal of Operational Research 210:301–309.
   Web of Science ®Google Scholar
• Lee, D., and K. Kim. 2012. Interactive weighting of bias and variance in dual response surface optimization. Expert Systems with Applications 39:5900–5906.
   Web of Science ®Google Scholar
• Lee, D., K. Kim, and M. Köksalan. 2012. An interactive method to multiresponse surface optimization based on pair wise comparisons. IIE Transactions 44:13–26.
   Web of Science ®Google Scholar
• Lee, D., and K. Kim. 2013. Determining the target value of ACICD to optimize the electrical characteristics of semiconductors using dual response surface optimization. Applied Stochastic Models in Business and Industry 29:377–386.
   Web of Science ®Google Scholar
• Lin, D. K. J., and W. Tu. 1995. Dual response surface optimization. Journal of Quality Technology 27:34–39.
   Web of Science ®Google Scholar
• Liu, J., J. Shi, and S. J. Hu. 2009. Quality assured setup planning based on the stream of variation model for multi-stage machining processes. IIE Transactions 41:323–334.
   Web of Science ®Google Scholar
• Lَpez-Fidalgo, J., C. Tommasi, and P. C. Trandafir. 2007. An optimal experimental design criterion for discriminating between non-normal models. Journal of the Royal Statistical Society, Series B: Statistical Methodology 69:231–242.
   Web of Science ®Google Scholar
• Maronna, R. A. R. D., R. D. Martin, and V. Yohai. 2006. Robust statistics. Chichester: John Wiley & Sons, p. 45–97.
   Google Scholar
• Montgomery, D. C. 2005. Design and Analysis of Experiments, 6th ed. Hoboken, NJ: John Wiley & Sons, p. 68–114.
   Google Scholar
• Moslemi, A., M. Bashiri, and S. T. A. Niaki. 2014. Robust estimation of multi-response surfaces considering correlation structure. Communications in Statistics-Theory and Methods 43: 4749–4765.
   Web of Science ®Google Scholar
• Mukherjee, I., and P. K. Ray. 2008. Optimal process design of two stage multiple responses grinding processes using desirability functions and metaheuristic technique. Applied Soft Computing Journal 8:402–421.
   Web of Science ®Google Scholar
• Mukherjee, I., and P. K. Ray. 2009. Quality improvement of multistage and multi-response grinding processes: an insight into two different methodologies for parameter optimisation. International Journal of Productivity and Quality Management 4:613–643.
   Google Scholar
• Mukherjee, I., and P. K. Ray. 2012. Ascendancy of a modified tabu search for multi-stage non-linear multiple response constrained optimisation problem. International Journal of Productivity and Quality Management 9:352–381.
   Google Scholar
• Niaki, S. T. A., and M. Davoodi. 2008. Designing a multivariate-multistage quality control system using artificial neural networks. International Journal of Production Research 47:251–271.
   Web of Science ®Google Scholar
• Noorossana, R., S. Davanloo Tajbakhsh, and A. Saghaei. 2009. An artificial neural network approach to multiple-response optimization. International Journal of Advanced Manufacturing Technology 40:1227–1238.
   Web of Science ®Google Scholar
• Pal, S., and S. K. Gauri. 2010. Multi-response optimization using multiple regression-based weighted signal-to-noise ratio (MRWSN). Quality Engineering 22:336–350.
   Web of Science ®Google Scholar
• Peterson, J. J. 2004. A posterior predictive approach to multiple response surface optimization. Journal of Quality Technology 36:139–153.
   Web of Science ®Google Scholar
• Phoomboplab, T., and D. Ceglarek. 2008. Process yield improvement through optimal design of fixture layout in 3D multi-station assembly systems. ASME Transactions, Journal of Manufacturing Science and Engineering 130(6), No. 061005.
   Web of Science ®Google Scholar
• Salmasnia, A., R. Baradaran 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 62:835–846.
   Web of Science ®Google Scholar
• Salmasnia, A., R. Baradaran Kazemzadeh, M. Seyyed-Esfahani, and T. H. Hejazi. 2013. Multiple response surface optimization with correlated data. International Journal of Advanced Manufacturing Technology 64:841–855.
   Web of Science ®Google Scholar
• Shah, H. K., D. C. Montgomery, and W. M. Carlyle. 2004. Response surface modeling and optimization in multiresponse experiments using seemingly unrelated regressions. Quality Engineering 16:387–397.
   Google Scholar
• Shi, J., and S. Zhou. 2009. Quality control and improvement for multistage systems: a survey. IIE Transactions 41:744–753.
   Web of Science ®Google Scholar
• Steuer, R. E. 1986. Multiple criteria optimization: theory, computation, and application. New York: John Wiley and Sons, p. 45–87.
   Google Scholar
• Su, C.-T., and L.-I. Tong 1997. Multi-response robust design by principal component analysis. Total Quality Management 8:409–416.
   Web of Science ®Google Scholar
• Taguchi, G., S. Chowdhury, Y. Wu, S. Taguchi, and H. Yano. 2005. Taguchi's quality engineering handbook. Hoboken- N.J. Livonia, Mich: John Wiley & Sons, ASI Consulting Group, p. 24–47.
   Google Scholar
• Tong, L. I., and K. L. Hsieh. 2000. A novel means of applying neural networks to optimize the multiresponse problem. Quality Engineering 13:11–18.
   Google Scholar
• Vining, G. G. 1998. A Compromise Approach to Multiresponse Optimization. Journal of Quality Technology 30:309–313.
   Web of Science ®Google Scholar
• Wang, J., Y. Ma, L. Ouyang, and Y. Tu. 2016. A new Bayesian approach to multi-response surface optimization integrating loss function with posterior probability. European Journal of Operational Research 249: 231–237.
   Web of Science ®Google Scholar
• Wazed, M. A., S. Ahmed, and Y. Nukman. 2011. Application of Taguchi a method to analyse the impacts of common process and batch size in multistage production system under uncertain conditions. European Journal of Industrial Engineering 5:215–231.
   Web of Science ®Google Scholar
• Wong, W. K. 1994. Comparing robust properties of A, D, E and G-optimal designs. Computational Statistics & Data Analysis 18:441 448.
   Google Scholar
• Wu, F.-C., and C.-C. Chyu. 2004. Optimization of correlated multiple quality characteristics robust design using principal component analysis. Journal of Manufacturing Systems 23:337–337.
   Web of Science ®Google Scholar
• Yang, T., P. Chou. 2005. Solving a multiresponse simulation-optimization problem with discrete variables using a multiple-attribute decision-making method. Mathematics and Computers in Simulation 68:9–21.
   Web of Science ®Google Scholar
• Zantek, P. F., G. P. Wright, and R. D. Plante. 2002. Process and product improvement in manufacturing systems with correlated stages. Management Science 48:591–606.
   Web of Science ®Google Scholar