Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
권호 표지
DOI QR코드

DOI QR Code

Dual Response Surface Optimization using Multiple Objective Genetic Algorithms

다목적 유전 알고리즘을 이용한 쌍대반응표면최적화

  • Lee, Dong-Hee (Division of interdisciplinary industrial studies, Hanyang University) ;
  • Kim, Bo-Ra (Division of interdisciplinary industrial studies, Hanyang University) ;
  • Yang, Jin-Kyung (Division of interdisciplinary industrial studies, Hanyang University) ;
  • Oh, Seon-Hye (Division of interdisciplinary industrial studies, Hanyang University)
  • 이동희 (한양대학교 산업융합학부) ;
  • 김보라 (한양대학교 산업융합학부) ;
  • 양진경 (한양대학교 산업융합학부) ;
  • 오선혜 (한양대학교 산업융합학부)
  • Received : 2016.12.08
  • Accepted : 2017.04.04
  • Published : 2017.06.15
Download PDF
⟨ Previous Next ⟩

Abstract

Dual response surface optimization (DRSO) attempts to optimize mean and variability of a process response variable using a response surface methodology. In general, mean and variability of the response variable are often in conflict. In such a case, the process engineer need to understand the tradeoffs between the mean and variability in order to obtain a satisfactory solution. Recently, a Posterior preference articulation approach to DRSO (P-DRSO) has been proposed. P-DRSO generates a number of non-dominated solutions and allows the process engineer to select the most preferred solution. By observing the non-dominated solutions, the DM can explore and better understand the trade-offs between the mean and variability. However, the non-dominated solutions generated by the existing P-DRSO is often incomprehensive and unevenly distributed which limits the practicability of the method. In this regard, we propose a modified P-DRSO using multiple objective genetic algorithms. The proposed method has an advantage in that it generates comprehensive and evenly distributed non-dominated solutions.

Keywords

References

  1. Arungpadang, T. R. and Kim, Y. J. (2013), A Study on Dual Response Approach Combining Neural Network and Genetic Algorithm, Journal of Korean Institute of Industrial Engineers, 39(5), 361-366. https://doi.org/10.7232/JKIIE.2013.39.5.361
  2. Byun, J. O. and Choi, Y. H. (2015), Stair Locomotion Method of Quadruped Robot Using Genetic Algorithm, The Journal of The Korea Institute of Electronic Communication Sciences, 10(9), 1039-1047. https://doi.org/10.13067/JKIECS.2015.10.9.1039
  3. Box, G. E. P. and Draper, N. R. (1987), Empirical Model Building and Response Surfaces, John Wiley & Sons, NY, United States.
  4. Charnes, A., Cooper, W., and Ferguson, R. (1955), Optimal estimation of executive compensation by linear programming, Management Science, 1(2), 138-151. https://doi.org/10.1287/mnsc.1.2.138
  5. Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T. (2000), A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization : NSGA-II, Lecture Notes in Computer Science, 1917, 849-858.
  6. Deb, K. (2001), Multi-objective optimization using evolutionary algorithms, John Wiley & Sons, Chichester, England.
  7. Horn, J., Nafpliotis, N., and Goldberg, D. E. (1994), A niched Pareto genetic algorithm for multiobjective optimization, IEEE, 1, 82-87.
  8. Jeong, J. H. and Ahn, C. W. (2016), An Automatic Rhythm and Melody Composition System Considering User Parameters and Chord Progression Based on a Genetic Algorithm, Journal of KIISE, 43(2), 204-211. https://doi.org/10.5626/JOK.2016.43.2.204
  9. Jeong, I. and Lee, D. (2016), Generating Evenly Distributed Nondominated Solutions in Dual Response Surface Optimization, working paper.
  10. Kim, B. and Yum, B. (2010), Development of Virtual Metrology Models in Semiconductor Manufacturing Using Genetic Algorithm and Kernel Partial Least Squares Regression, Journal of the Korean Institute of Industrial Engineers, 23(3), 229-238.
  11. Kim, D. and Rhee, S. (2003), Optimization of GMA welding process using the dual response approach, International Journal of Production Research, 41(18), 4505-4516. https://doi.org/10.1080/0020754031000/595800
  12. Kim, Y. G. (2011), Evolutionary algorithms, Chonnam National University Press, Gwangju, Korea.
  13. Kim, W. W. (2012), A Multi-Objective Genetic Algorithm Using Sequential Surrogate Models, Hanyang University Graduate School.
  14. Laumanns, M., Rudolph, G., and Schwefel, H. P. (1998), A spatial predator-prey approach to multi-objective optimization : A preliminary study, Lecture Notes in Computer Science, 1498, 241-249.
  15. Lee, D., Jeong, I., and Kim, K. (2010), A posterior preference articulation approach to dual response surface optimization, IIE Transactions, 42(2), 161-171. https://doi.org/10.1080/07408170903228959
  16. Lee, D. and Kim, K. (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(4), 377-386. https://doi.org/10.1002/asmb.1973
  17. Lee, D., Kim, K., and Koksalan, M. A. (2011), posterior preference articulation approach to multiresponse surface optimization, European Journal of Operational Research, 210(2), 301-309. https://doi.org/10.1016/j.ejor.2010.09.032
  18. Lee, D., Jeong, I., and Kim, K. (2013), Methods and Applications of Dual Response Surface Optimization : A Literature Review, Journal of the Korean Institute of Industrial Engineers, 39(5), 342-350. https://doi.org/10.7232/JKIIE.2013.39.5.342
  19. Lee, S. M. (1972), Goal Programming for Decision Analysis, Auerbach Publishers, Philadelphia.
  20. Lee, S. Y. (2006), Multiobjective Optimization Using Simulated Annealing and Multiobjective Metrics, Korea Advanced Institute of Science and Technology.
  21. Lin, D. and Tu, W. (1995), Dual response surface optimization, Journal of Quality Technology, 27(1) 34-39. https://doi.org/10.1080/00224065.1995.11979556
  22. Miettinen, K. (1999), Nonlinear Multiobjective Optimization, Kluwer, Boston.
  23. Osyczka, A. and Kundu, S. (1995), A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm, Structural optimization, 10(2), 94-99. https://doi.org/10.1007/BF01743536
  24. Park, J. and Lee, D. (2016), Optimizing multiple response variables of chemical and mechanical planarization process for semiconductor fabrication using a clustering method, International Journal of Industrial Engineering, 23(5), 283-293.
  25. Park, K. J. and Lee, C. S. (2011), Metric Information and Pareto Dominance Concept in Multi-Objective Particle Swarm Optimization Using a Supply Chain Model and a Known Function, Journal of the Korean Society of Supply Chain Management, 11(2), 31-39.
  26. Schaffer, J. D. (1984), Some Experiments in Machine Learning Using Vector Evaluated Genetic Algorithms, Ph.D. Thesis, Vanderbilt University, TN, United States.
  27. Seo, J. H., Lee, D. H., Lee, K. C., Kim, K. J., and Kim, K. J. (2016), Optimizing a blend of a mixture slurry in chemical mechanical planarization for advanced semiconductor manufacturing using a posterior preference articulation approach to dual response surface optimization, Applied Stochastic Models in Business and Industry, 32(5), 648-659. https://doi.org/10.1002/asmb.2185
  28. Srinivas, N. and Deb, K. (1994), Multi-objective function optimization using non-dominated sorting genetic algorithms, Evolutionary Computation Journal, 2(3), 221-248. https://doi.org/10.1162/evco.1994.2.3.221
  29. Veldjuizen, D. A. V. and Lamont, G. B. (2000), On measuring multiobjective evolutionary algorithm performance, Evolutionary Computation, 1, 204-211.
  30. Vining, G. G. and Myers, R. H. (1990), Combining Taguchi and response surface philosophies : a dual response approach, Journal of Quality Technology, 22(1), 38-45. https://doi.org/10.1080/00224065.1990.11979204
  31. Yum, B., Kim, S., Seo, S., Byun, J., and Lee, S. (2013), The Taguchi Robust Design Method : Current Status and Future Directions, Journal of Korean Institute of Industrial Engineers, 39(5), 325-341. https://doi.org/10.7232/JKIIE.2013.39.5.325
  32. Zitzler, E. and Thiele, L. (1998), An evolutionary algorithm for multiobjective optimization : The strength Pareto approach, Technical Report 43, Zurich, Switzerland: Computer Engineering and Networks Laboratory(TIK), Swiss Federal Institute of Technology(ETH).
  33. Zitzler, E. (1999), Evolutionary Algorithms for Multiobjective Optimization : Methods and Applications, Ph. D. Thesis, Zurich, Switzerland. Swiss Federal Institute of Technology(ETH) (Dissertation ETH No. 13398).
  34. Zitzler, E. and Laumanns, M. (2001), SPEA2 : Improving the strength Pareto evolutionary algorithm. Eidgenossische Technische HochschuleZurich(ETH), Institut fur Technische Informatik und Kommunikationsnetze(TIK).