Multiple response optimization: A global criterion-based method

Journal of Chemometrics

Volumn 24, Issue 6, 2010, Pages 333-342

  Costa, Nuno Ricardo ^a,b   Pereira, Zulema Lopes ^b  

^a INSTITUTO POLITÉCNICO DE SETÚBAL   (Portugal)

^b UNIVERSIDADE NOVA DE LISBOA   (Portugal)

Author keywords

Desirability Function; Distance Function; Loss Function; Response Surface

Indexed keywords

DESIRABILITY FUNCTION; DISTANCE FUNCTIONS; GLOBAL CRITERIA; IMPLEMENTATION STEPS; LOSS FUNCTIONS; MULTI-RESPONSE PROBLEM; MULTIPLE RESPONSE OPTIMIZATION; OPTIMIZATION MEASURES; QUALITY CHARACTERISTIC; RESPONSE SURFACE;

SURFACE PROPERTIES;

EID: 77954023025     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.1312     Document Type: Article

References (46)

1. Frey D, Wang H. Adaptive one-factor-at-a-time experimentation and expected value of improvement. Technometrics 2006; 48: 418-431.
2. Box G, Draper N. Empirical Model Building and Response Surfaces. John Wiley & Sons: New York, 1987.
3. Khuri A, Cornell J. Response Surfaces, 2nd edn. Dekker: New York, 1996.
4. Myers R, Montgomery D. Response Surface Methodology: Process and product optimization using designed experiments, 2nd edn. Wiley: New Jersey, 2002.
5. Ortiz F, Simpson J, Pignatiello J. A genetic algorithm approach to multiple-response optimization. J. Qual. Technol. 2004; 36: 432-450.
6. Derringer G, Suich R. Simultaneous optimization of several response variables. J. Qual. Technol. 1980; 12: 214-218.
7. Derringer G. A balancing act: optimizing a product's properties. Qual. Prog. 1994; June: 51-58.
8. Köksoy O. A nonlinear programming solution to robust multiresponse quality problem. Appl. Math. Comput. 2008; 196: 603-612.
9. Kros J, Mastrangello C. Comparing multi-response design methods with mixed responses. Qual. Reliab. Eng. Int. 2004; 20: 527-539.
10. Chiao C, Hamada M. Analysis of experiments with correlated multiple responses. J. Qual. Technol. 2001; 33: 451-465.
11. Ko Y-H, Kim K-J, Jun C-H. A new loss function-based method for multiresponse optimization. J. Qual. Technol. 2005; 37: 50-59.
12. Xu K, Lin D, Tang L, Xie M. Multiresponse systems optimization using a goal attainment approach. IIE Trans. 2004; 36: 433-445.
13. Khuri A, Conlon M. Simultaneous optimization of multiple responses represented by polynomial regression functions. Technometrics 1981; 23: 363-375.
14. Vining G. A compromise approach to multiresponse optimization. J. Qual. Technol. 1998; 30: 309-313.
15. Weicek M, Ehrgott M, Fadel G, Figueira J. Multiple criteria decision making for engineering. Omega 2008; 36: 337-339.
16. Jeyapaul R, Shahabudeen P, Krishnaiah K. Quality management research by considering multi-response problems in the Taguchi method-a review. Int. J. Adv. Manuf. Technol. 2005; 26: 1331-1337.
17. Fogliatto F. Multiresponse optimization of products with functional quality characteristics. Qual. Reliab. Eng. Int. 2008; 24: 927-939.
18. Kim K, Lin D. Simultaneous optimization of multiple responses by maximizing exponential desirability functions. Appl. Stat. C 2000; 49: 311-325.
19. Köksalan M, Plante R. Interactive multicriteria optimization for multiple-response product and process design. Manuf. Serv. Oper. Manag. 2003; 5: 334-347.
20. Tabucanon M. Multiple Criteria Decision Making in Industry, Studies in Production and Engineering Economics. Elsevier: Amsterdam, 1988; 35.
21. Wurl R, Albin S. A comparison of multiresponse optimization: sensitivity to parameter selection. Qual. Eng. 1999; 11: 405-415.
22. Messac A, Sundararaj G, Tappeta R, Renaud J. Ability of objective functions to generate points on non-convex pareto frontiers. AIAA J. 2000; 38: 1084-1091.
23. Carlyle W, Montgomery D, Runger K. Optimization problems and methods in quality control and improvement. J. Qual. Technol. 2000; 32: 1-17.
24. Tanco M, Viles E, Ilzarbe L, Alvarez M. Implementation of design of experiments projects in industry. Appl. Stoch. Models Data Anal. 2009; 25: 478-505.
25. Anderson-Cook C. Design of experiments: a single experiment or sequential learning. Qual. Prog. 2007; March: 71-73.
26. Costa N, Pires A, Ribeiro C. Guidelines to help practitioners of design of experiments. TQM Mag. 2006; 18: 386-399.
27. Bisgaard S. Proposals: a mechanism for achieving better experiments. Qual. Eng. 1999; 11: 645-649.
28. Coleman D, Montgomery D. A systematic approach to planning for a designed industrial experiment. Technometrics 1993; 35: 1-12.
29. Fogliatto F, Albin L. Variance of predicted response as an optimization criterion in multiresponse experiments. Qual. Eng. 2000; 12: 523-533.
30. Shah H, Montgomery D, Matthew W. Response surface modeling and optimization in multiresponse experiments using seemingly unrelated regressions. Qual. Eng. 2004; 16: 387-397.
31. Myers R, Montgomery D, Anderson-Cook C. Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 3rd edn. Wiley: New Jersey, 2008; 36-46.
32. Kim K, Lin D. Dual response surface optimization: a fuzzy modelling approach. J. Qual. Technol. 1998; 30: 1-10.
33. Neter J, Kutner M, Nachtsheim C, Wasserman W. Applied Linear Regression Models, 3rd edn. Irwin: Boston, 1996; 336-347.
34. Myers R. Response surface methodology: current status and future directions. J. Qual. Technol. 1999; 31: 30-44.
35. Schmidt R, Illingworth B, Deng J, Cornell J. Multiple regression and response surface analysis of the effects of calcium chloride and cysteine on heat-induced whey protein gelation. J. Agric. Food Chem. 1979; 27: 529-532.
36. Pignatiello J. Strategies for robust multiresponse. IIE Trans. 1993; 25: 5-15.
37. Ribeiro J, Fogliatto F, Caten C. Minimizing manufacturing and quality costs in multiresponse optimization. Qual. Eng. 2001; 13: 191-201.
38. Wu F, Chyu C. Optimization of robust design for multiple quality characteristics. Int. J. Prod. Res. 2004; 42: 337-354.
39. Minkoo L, Kim I. Separate response surface modeling for multiple response optimization: multivariate loss function approach. Int. J. Ind. Eng. 2007; 14: 227-235.
40. Bhamare S, Yadav O, Rathore A. A hybrid quality loss function-based multi-objective design optimization approach. Qual. Eng. 2009; 21: 277-289.
41. Kim K, Lin D. Optimization of multiple responses considering both location and dispersion effects. Eur. J. Oper. Res. 2006; 169: 133-145.
42. Lee M-S, Kim K-J. Expected desirability function: consideration of both location and dispersion effects in desirability function approach. Qual. Technol. Quant. Manag. 2007; 4: 365-377.
43. Kovach J, Cho B. Development of a multidisciplinary-multiresponse robust design optimization model. Eng. Optim. 2008; 40: 805-819.
44. Kovach J, Cho B, Antony J. Development of an experiment-based robust design paradigm for multiple quality characteristics using physical programming. Int. J. Adv. Manuf. Technol. 2008; 35: 1100-1112.
45. Yi{dotless}ldi{dotless}z A. A new design optimization framework based on immune algorithm and Taguchi's method. Comput. Ind. 2009; 60: 613-620.
46. Ayvaz M, Tamer K, Ali H, Ceylan H, Gurarslan G. Hybridizing the harmony search algorithm with a spreadsheet 'Solver' for solving continuous engineering optimization problems. Eng. Optim. 2009; 41: 1119-1144.