DOI QR코드

DOI QR Code

의약품개발공정에서의 Augmented weighted Tchebycheff 모델링 및 강건설계최적화

Augmented Weighted Tchebycheff Modeling and Robust Design Optimization on a Drug Development Process

  • ;
  • 신상문 (동아대학교 산업경영공학과)
  • Ho, Le Tuan (Department of Industrial and Management Systems Engineering, Dong-A University) ;
  • Shin, Sangmun (Department of Industrial and Management Systems Engineering, Dong-A University)
  • 투고 : 2013.07.31
  • 심사 : 2013.09.16
  • 발행 : 2013.10.15

초록

The quality of the products/processes has been improved remarkably since robust design (RD) methodology is applied into the practice manufacturing processes. A model building method based on the dual responses methods for multiple and time oriented responses on a drug development process is employed in this paper instead of the previous methods that handle the static nature of data and single response. Subsequently, the optimal solutions of a multiple and time series RD problem are obtained by using the proposed augmented weighted Tchebycheff method that has a significant flexibility on assigning weights. Finally, a pharmaceutical case study associated with a generic drug development process is conducted in order to illustrate the efficient optimal solutions from the proposed model.

키워드

참고문헌

  1. Box, G. (1988), Signal-to-Noise Ratios, Performance Criteria, and Transformations, Technometrics, 30(1), 1-17. https://doi.org/10.1080/00401706.1988.10488313
  2. Castillo, E. and Montgomery, D. C. (1993), A nonlinear programming solution to the dual response problem, Journal of Quality Technology, 25, 199-204. https://doi.org/10.1080/00224065.1993.11979454
  3. Cho, B. R., Philips, M. D., and Kapur, K. C. (1996), Quality improvement by RSM modeling for robust design, The 5th Industrial Engineering Research Conference, Minneapolis, MN, 650-655.
  4. Cho, B. R., Shin, S. M., Choi, Y., and Kovach, J. (2009), Development of a Multidisciplinary Optimization Process for Designing Optimal Pharmaceutical Formulations with Constrained Experimental Regions, International Journal of Advanced Manufacturing Technology, 44, 841-853. https://doi.org/10.1007/s00170-008-1895-5
  5. Choi, D. H., Shin, S. M., Truong, N. K. V., Jung, Y. J., Chu, K. R., and Jeong, S. H. (2012), A new experimental design method to optimize formulations focusing on a lubricant for hydrophilic matrix tablets, Journal of Drug Development and Industrial pharmacy, 38(9), 1117-1127. https://doi.org/10.3109/03639045.2011.641563
  6. Copeland, K. A. F. and Nelson, P. R. (1996), Dual Response Optimization via Direct Function Minimization, Journal of Quality Technology, 28 (3), 331-336. https://doi.org/10.1080/00224065.1996.11979683
  7. Dachert, K., Gorski, J., and Klamroth, K. (2012), An augmented weighted Tchebycheff method with adaptively chosen parameters for discrete bicriteria optimization problems, Computers and Operations Research, 39(12), 2929-2943. https://doi.org/10.1016/j.cor.2012.02.021
  8. Del Castillo, E. and Montgomery, D. C. (1993), A nonlinear programming solution to the dual response problem, Journal of Quality Technology, 25, 199-204. https://doi.org/10.1080/00224065.1993.11979454
  9. Ding, R., Lin, D. K. J., and Wei, D. (2004), Dual Response Surface Optimization : A Weighted MSE Approach, Quality Engineering, 16(3), 377-385. https://doi.org/10.1081/QEN-120027940
  10. Ehrgott, M. (2006), A discussion of scalarization techniques for multiple objective integer programming, Ann Oper Res, 147, 343-360. https://doi.org/10.1007/s10479-006-0074-z
  11. Goethals, P. L. and Cho, B. R. (2011), The development of a robust design methodology for time-oriented dynamic quality characteristics with a target profile, Quality and Reliability Engineering International, 27, 403-414. https://doi.org/10.1002/qre.1122
  12. Hsieh, K., Tong, L., Chiu, H., and Yeh, H. (2005), Optimization of a multiresponse problem in Taguchi's dynamic system, Computers and Industrial Engineering, 49, 556-571. https://doi.org/10.1016/j.cie.2005.08.002
  13. Kasimbeyli, R. (2013), A conic scalarization method in multiobjective optimization, J Glob Optim, 56, 279-297. https://doi.org/10.1007/s10898-011-9789-8
  14. Kim, Y. J. and Cho, B. R. (2002), Development of Priority-Based Robust Design, Quality Engineering, 14, 355-363. https://doi.org/10.1081/QEN-120001874
  15. Kim, K. J. and Lin, D. K. J. (1998), Dual response surface optimization : A fuzzy modeling approach, Journal of Quality Technology, 30, 1-10. https://doi.org/10.1080/00224065.1998.11979814
  16. Koksoy, O. and Doganaksoy, N. (2003), Joint optimization of mean and standard deviation using response surface methods, Journal of Quality Technology, 35, 239-252. https://doi.org/10.1080/00224065.2003.11980218
  17. Kovach, J. and Cho, B. R. (2008), Development of a multidisciplinarymultiresponse robust design optimization model, Engineering Optimization, 40, 805-819. https://doi.org/10.1080/03052150802046304
  18. Leon, R. V., Shoemaker, A. C., and Kacker, R. N. (1987), Performance measures independent of adjustment-An explanation and extension of Taguchi's signal-to-noise ratios, Technometrics, 29(3), 253-265. https://doi.org/10.1080/00401706.1987.10488231
  19. Lin, D. K. J. and Tu, W. (1995), Dual response surface optimization, Journal of Quality Technology, 27, 34-39. https://doi.org/10.1080/00224065.1995.11979556
  20. Marler, R. T. and Arora, J. S. (2004), Survey of multiobjective optimization methods for engineering, Struct Multidisc Optim, 26, 369-395. https://doi.org/10.1007/s00158-003-0368-6
  21. Miettinen, K. (1999), Nonlinear multiobjective optimization, Boston : Kluwer Academic Publishers.
  22. Miettinen, K. and Makela, M. M. (2002), On scalarizing functions in multiobjective optimization, OR Spectrum, 24, 193-213. https://doi.org/10.1007/s00291-001-0092-9
  23. Myers, R. H. and Montgomery, D. C. (2002), Response surface methodology : process and product optimization using designed experiments (2nd ed.). J. Wiley, New York.
  24. Nair, V. N. (1992), Taguchi's Parameter Design : A Panel Discussion, Technometrics, 34, 127-161. https://doi.org/10.1080/00401706.1992.10484904
  25. Nha, V. T., Shin, S. M., and Jeong, S. H. (2013), Lexicographical dynamic goal programming approach to a robust design optimization within the pharmaceutical environment, European Journal of Operational Research, 229(2), 505-517. https://doi.org/10.1016/j.ejor.2013.02.017
  26. Ralphs, T. K., Saltzman, M. J., and Wiecek., M. M. (2006), An improved algorithm for solving biobjective integer programs, Annals of Operations Research, 147(1), 43-70. https://doi.org/10.1007/s10479-006-0058-z
  27. Reddy, K., Nishina1, A., and Babu, S. (1998), Unificationofrobust design and goal programming for multiresponse optimization-a case study, Quality and Reliability Engineering International, 13(6), 371-383.
  28. Robinson, T. J., Wulff, S. S., Montgomery, D. S., and Khuri, A. I. (2006), Robust parameter design using generalized linear mixed models, Journal of Quality Technology, 38, 65-75. https://doi.org/10.1080/00224065.2006.11918585
  29. Shin, S. M. and Cho, B. R. (2005), Bias-specified robust design optimization and an analytical solutions, Computers and Industrial Engineering, 48, 129-148. https://doi.org/10.1016/j.cie.2004.07.011
  30. Shin, S., Choi, D. H., Truong, N. K. V., Kim, N. A., Chu, K. R., and Jeong, S. H. (2011), Time-oriented experimental design method to optimize hydrophilic matrix formulations with gelation kinetics and drug release profiles, International Journal of Pharmaceutics, 407, 53-62. https://doi.org/10.1016/j.ijpharm.2011.01.013
  31. Steuer, R. E. (1986), Multiple Criteria Optimization : Theory, Computation and Application, Wiley, New York, NY.
  32. Steuer, R. E. and Choo, E. U. (1983), An interactive weighted Tchebycheff procedure for multiple objective programming, Mathematical Programming, 26, 326-344. https://doi.org/10.1007/BF02591870
  33. Tang, L. C. and Xu, K. (2002), A Unified Approach for Dual Response Surface Optimization, Journal of Quality Technology, 34(4), 437-447. https://doi.org/10.1080/00224065.2002.11980175
  34. Tind, J. and Wiecek, M. M. (1999), Augmented Lagrangian and Tchebycheff approaches in multiple objective programming, Journal of Global Optimization, 14, 251-266. https://doi.org/10.1023/A:1008314306344
  35. Truong, N. K. V., Shin, S. M., and Jeong, S. H. (2011), Integrating Inverse problem to robust design for a generic drug development process, Journal of the Korean Society for Quality Management, 39(3), 365-376.
  36. Tsui, K. L. (1992), An overview of Taguchi method and newly developed statistical methods for robust design, IIE Transactions, 24(5), 44-57. https://doi.org/10.1080/07408179208964244
  37. Vining, G. G. and Myers, R. H. (1990), Combining Taguchi and Response Surface Philosophies : A dual Response Approach, Journal of Quality Technology, 22, 38-45. https://doi.org/10.1080/00224065.1990.11979204
  38. Zadeh, L. A. (1963), Optimality and non-scalar-valued performance criteria, IEEE Trans, Autom. Control AC-8, 59-60.
  39. Zionts, S. (1988), Multiple criteria mathematical programming : an updated overview and several approaches. In : Mitra, G. (ed.) Mathematical Models for Decision Support, 135-167. Berlin : Springer-Verlag.