Browse > Article
http://dx.doi.org/10.7469/JKSQM.2017.45.2.191

An Interactive Process Capability-Based Approach to Multi-Response Surface Optimization  

Jeong, In-Jun (Department of Business Administration, Daegu University)
Publication Information
Journal of Korean Society for Quality Management / v.45, no.2, 2017 , pp. 191-207 More about this Journal
Abstract
Purpose: To develop an interactive version of the conventional process capability-based approach, called 'Interactive Process Capability-Based Approach (IPCA)' in multi-response surface optimization to obtain a satisfactory compromise which incorporates a decision maker(DM)'s preference information precisely. Methods: The proposed IPCA consists of 4 steps. Step 1 is to obtain the estimated process capability indices and initialize the parameters. Step 2 is to maximize the overall process capability index. Step 3 is to evaluate the optimization results. If all the responses are satisfactory, the procedure stops with the most preferred compromise solution. Otherwise, it moves to Step 4. Step 4 is to adjust the preference parameters. The adjustment can be made in two modes: relaxation and tightening. The relaxation is to make the importance of one of the satisfactory responses lower, which is implemented by decreasing its weight. The tightening is to make the importance of one of the unsatisfactory responses higher, which is implemented by increasing its weight. Then, the procedure goes back to Step 2. If there is no response to be adjusted, it stops with the unsatisfactory compromise solution. Results: The proposed IPCA was illustrated through a multi-response surface problem, colloidal gas aphrons problem. The illustration shows that it can generate a satisfactory compromise through an interactive procedure which enables the DM to provide his or her preference information conveniently. Conclusion: The proposed IPCA has two major advantages. One is to obtain a satisfactory compromise which is faithful to the DM preference structure. The other is to make the DM's participation in the interactive procedure easier by using the process capability index in judging satisfaction/unsatisfaction. The process capability index is very familiar with quality practitioners as well as indicates the process performance levels numerically.
Keywords
Multi-Response Surface Optimization; Process Capability Indices; Interactive Approach;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Ames, A., Mattucci, N., McDonald, S., Szonyi, G., and Hawkins, D. 1997. "Quality Loss Function for Optimization Across Multiple Response Surfaces." Journal of Quality Technology 29:339-346.   DOI
2 Ardakani, M. K., and Wulff, S. S. 2013. "An Overview of Optimization Formulations for Multiresponse Surface Problems." Quality and Reliability Engineering International 29(1):3-16.   DOI
3 Barton, R. S., and Tsui, K. L. 1991. "Multivariate Yield Maximization Using CAD/CAE Models: Efficient Approximations Based on Mean and Variance." Design Theory and Methodology (ASME) 31:31-35.
4 Biles, W. E. 1975. "A Response Surface Methods for Experimental Optimization of Multi-Response Processes." Industrial and Engineering Chemistry Process Design and Deployment 14:152-158.   DOI
5 Chan, L. K., Cheng, S. W., and Spiring, F. A. 1988. "A New Measure of Process Capability Cpm." Journal of Quality Technology 20(3):162-175.   DOI
6 Chiao, C., and Hamada, M. 2001. "Analyzing Experiments with Correlated Multiple Responses." Journal of Quality Technology 33:451-465.   DOI
7 Ch'ng, C. K., Quah, S. H., and Low, H. C. 2005. "Index Cpm* in Multiple Response Optimization." Quality Engineering 17:165-171.
8 Cho, M., and Lim, T. 2002. "The Relation between the Process Capability Index and the Quality Assurance Level Considering Various Conditions." Journal of the Korean Society for Quality Management 30(2):130-151.
9 Del Castillo, E., Montgomery, D. C., and McCarville, D. R. 1996. "Modified Desirability Functions for Multiple Response Optimization." Journal of Quality Technology 28:337-345.   DOI
10 Derringer, G. 1994. "A Balancing Act: Optimizing a Product's Properties." Quality Progress 51-57.
11 Derringer, G. and Suich, R. 1980. "Simultaneous Optimization of Several Response Variables." Journal of Quality Technology 12:214-219.   DOI
12 Harrington, E., Jr. 1965. "The Desirability Function." Industrial Quality Control 21:494-498.
13 Hsiang, T. C., and Taguchi, G. 1985. A Tutorial on Quality Control and Assurance - the Taguchi methods. In: ASA Annual Meeting, Las Vegas, Nevada, USA.
14 Hwang, C., Masud, A. S. M., Paidy., S. R., and Yoon, K. 1979. Multiple Objective Decision Making. Methods and Applications (Lecture Notes in Economics and Mathematical Systems). Berlin: Springer-Verlag.
15 Juran, J. M. 1974. Quality Control Handbook (3rd ed.). New York: McGraw-Hill.
16 Jauregi, P., Gilmour, S., and Varley, J., 1997. "Characterisation of Colloidal Gas Aphrons for Subsequent Use for Protein Recovery." Chemical Engineering Journal 65:1-11.   DOI
17 Jeong, I. 2011. "Multiresponse Optimization: A Literature Review and Research Opportunities." Journal of the Korean Society for Quality Management 39(3):377-390.
18 Del Castillo, E. 1996. "Multiresponse Process Optimization via Constrained Confidence Regions." Journal of Quality Technology 28:61-70.   DOI
19 Kane, V. E. 1986. "Process Capability Indices." Journal of Quality Technology 18(1):41-52.   DOI
20 Khuri, A. I. 1996. "Multiresponse Surface Methodology." Handbook of Statistics: Design and Analysis of Experiment (Vol. 13) (eds. A. Ghosh and C. R. Rao) 377-406.
21 Khuri, A. I., and Conlon, M. 1981. "Simultaneous Optimization of Multiple Responses Represented by Polynomial Regression Functions." Technometrics 23:363-375.   DOI
22 Kim, K., and Lin, D. 2000. "Simultaneous Optimization of Mechanical Properties of Steel by Maximizing Exponential Desirability Functions." Journal of Royal Statistical Society-Series C 49:311- 325.   DOI
23 Kim, K., and Lin, D. 2006. "Optimization of Multiple Responses Considering Both Location and Dispersion Effects." European Journal of Operational Research 169:133-145.   DOI
24 Ko, Y., Kim, K., and Jun, C. 2005. "A New Loss Function- Based Method for Multiresponse Optimization." Journal of Quality Technology 37:50-59.   DOI
25 Myers, R. H., Khuri, A., and Carter, W. H., Jr. 1989. "Response Surface Methodology: 1966-1988." Technometrics 31(2):137-157.
26 Miro-Quesada, G., Del Castillo, E., and Peterson, J. J. 2004. "A Bayesian Approach for Multiple Response Surface Optimization in the Presence of Noise Variables." Journal of Applied Statistics 31:251-270.   DOI
27 Myers, R. H. 1999. "Response Surface Methodology - Current Status and Future Directions." Journal of Quality Technology 31(1):30-44.   DOI
28 Myers, R. H., and Carter, W. H. 1973. "Response Surface Techniques for Dual Response Systems." Technometrics 15:301-317.   DOI
29 Myers, R. H., Montgomery, D. C., Vining, G. G., Borror, C. M., and Kowalski, S. M. 2004. "Response Surface Methodology: A Retrospective and Literature Survey." Journal of Quality Technology 36(1):53-77.   DOI
30 Park, Y. T. 2014. Quality Management. Seoul: Korean Standards Association Media.
31 Peterson, J. J. 2004. "A Posterior Predictive Approach to Multiple Response Surface Optimization." Journal of Quality Technology 36:139-153.   DOI
32 Plante, R. D. 1999. "Multicriteria Models for the Allocation of Design Parameter Targets." European Journal of Operational Research 115:98-112.   DOI
33 Steuer, R. E. 1986. Multiple Criteria Optimization: Theory, Computation, and Application. New York: John Wiley & Sons.
34 Sullivan, L. P. 1984. "Targeting Variability - A New Approach to Quality." Quality Progress 17(7):15-21.
35 Sullivan, L. P. 1985. "Letters." Quality Progress 18(4):7-8.
36 Pignatiello, J. 1993. "Strategies for Robust Multiresponse Quality Engineering." IIE Transactions 25:5-15.
37 Tsui, K. L. 1999. "Robust Design Optimization for Multiple Characteristic Problems." International Journal of Production Research 37:433-445.   DOI
38 Vining, G. 1998. "A Compromise Approach to Multiresponse Optimization." Journal of Quality Technology 30:309-313.   DOI