본문 바로가기
KERD
논문KCI 등재

대화식 절차를 활용한 공정능력지수 기반 다중반응표면 최적화

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

0

조회

0

다운로드

3

인용

0

참고문헌

키워드

주제분류학제간연구

초록

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.

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.

참고문헌 (38)

  1. Ames, A., "Quality Loss Function for Optimization Across Multiple Response Surfaces", Journal of Quality Technology, 29, pp.339~346 (1997)
  2. Ardakani, M. K., "An Overview of Optimization Formulations for Multiresponse Surface Problems", Quality and Reliability Engineering International, 29(1), pp.3~16 (2013)
  3. Barton, R. S., "Multivariate Yield Maximization Using CAD", CAE Models: Efficient Approximations Based on Mean and Variance, 1991, pp.31, 31~35
  4. Biles, W. E, "A Response Surface Methods for Experimental Optimization of Multi-Response Processes", Industrial and Engineering Chemistry Process Design and Deployment, 14, pp.152~158 (1975)
  5. Chan, L. K., "A New Measure of Process Capability Cpm", Journal of Quality Technology, 20(3), pp.162~175 (1988)
  6. Chiao, C., "Analyzing Experiments with Correlated Multiple Responses", Journal of Quality Technology, 33, pp.451~465 (2001)
  7. Chng, C. K., "Index Cpm* in Multiple Response Optimization", Quality Engineering, 17, pp.165~171 (2005)
  8. 조문수, "다양한 상황을 고려한 공정능력지수와 품질보증수준의 관계", 품질경영학회지, 30(2), pp.10~151 (2002), 한국품질경영학회
  9. Del Castillo, E, "Multiresponse Process Optimization via Constrained Confidence Regions", Journal of Quality Technology, 28, pp.61~70 (1996)
  10. Del Castillo, E., "Modified Desirability Functions for Multiple Response Optimization", Journal of Quality Technology, 28, pp.337~345 (1996)
  11. Derringer, G., "A Balancing Act: Optimizing a Product's Properties", Quality Progress, pp.51~57 (1994)
  12. Derringer, G., "Simultaneous Optimization of Several Response Variables", Journal of Quality Technology, 12, pp.214~219 (1980)
  13. Harrington, E., Jr., "The Desirability Function", Industrial Quality Control, 21, pp.494~498 (1965)
  14. Hsiang, T. C., "A Tutorial on Quality Control and Assurance — the Taguchi methods", ASA Annual Meeting (1985)
  15. Hwang, C. L., "Multiple Objective Decision Making-Methods and Applications", Lecture Notes in Economics and Mathematical Systems (1979), Springer-Verlag
  16. Jauregi, P., "Characterisation of Colloidal Gas Aphrons for Subsequent Use for Protein Recovery", Chemical Engineering Journal, 65, pp.1~11 (1997)
  17. 정인준, "Multiresponse Optimization: A Literature Review and Research Opportunities", 품질경영학회지, 39(3), pp.377~390 (2011), 한국품질경영학회
  18. Juran J. M., "Quality Control, Handbook" (1974), McGraw-Hill
  19. Kane, V. E, "Process Capability Indices", Journal of Quality Technology, 18(1), pp.41~52 (1986)
  20. Khuri, A. I., "Handbook of Statistics: Design and Analysis of Experiment (Vol. 13)", pp.377~406 (1996)
  21. Khuri, A. I., "Simultaneous Optimization of Multiple Responses Represented by Polynomial Regression Functions", Technometrics, 23, pp.363~375 (1981)
  22. Kim, K., "Simultaneous Optimization of Mechanical Properties of Steel by Maximizing Exponential Desirability Functions", Journal of Royal Statistical Society-Series C, 49, pp.311~325 (2000)
  23. Kim, K., "Optimization of Multiple Responses Considering Both Location and Dispersion Effects", European Journal of Operational Research, 169, pp.133~145 (2006)
  24. Ko, Y., "A New Loss Function-Based Method for Multiresponse Optimization", Journal of Quality Technology, 37, pp.50~59 (2005)
  25. Miró-Quesada, G., "A Bayesian Approach for Multiple Response Surface Optimization in the Presence of Noise Variables", Journal of Applied Statistics, 31, pp.251~270 (2004)
  26. Myers, R. H, "Response Surface Methodology-Current Status and Future Directions", Journal of Quality Technology, 31(1), pp.30~44 (1999)
  27. Myers, R. H., "Response Surface Techniques for Dual Response Systems", Technometrics, 15, pp.301~317 (1973)
  28. Myers, R. H., "Response Surface Methodology : 1966-1988", Technometrics, 31(2), pp.137~157 (1989)
  29. Myers, R. H., "Response Surface Methodology : A Retrospective and Literature Survey", Journal of Quality Technology, 36(1), pp.53~77 (2004)
  30. Park, Y. T., "Quality Management" (2014), Korean Standards Association Media
  31. Peterson, J. J, "A Posterior Predictive Approach to Multiple Response Surface Optimization", Journal of Quality Technology, 36, pp.139~153 (2004)
  32. Pignatiello, J, "Strategies for Robust Multiresponse Quality Engineering", IIE Transactions, 25, pp.5~15 (1993)
  33. Plante, R. D, "Multicriteria Models for the Allocation of Design Parameter Targets", European Journal of Operational Research, 115, pp.98~112 (1999)
  34. Steuer, R. E., "Multiple Criteria Optimization: Theory, Computation, and Application" (1986), John Wiley & Sons
  35. Sullivan, L. P, "Targeting Variability — A New Approach to Quality", Quality Progress, 17(7), pp.15~21 (1984)
  36. Sullivan, L. P, "Letters", Quality Progress, 18(4), pp.7~8 (1985)
  37. Tsui, K. L, "Robust Design Optimization for Multiple Characteristic Problems", International Journal of Production Research, 37, pp.433~445 (1999)
  38. Vining, G, "A Compromise Approach to Multiresponse Optimization", Journal of Quality Technology, 30, pp.309~313 (1998)

관련 논문AI추천

이 간행물의 다른 논문 (8건)