Robust Design in Multiobjective Systems using Taguchi’s Parameter Design Approach and a Pareto Genetic Algorithm
DOI:
https://doi.org/10.17533/udea.redin.15439Keywords:
Pareto genetic algorithms, multiobjective evolutionary algorithm, parameter designAbstract
We present a Pareto Genetic Algorithm (PGA), which finds the Pareto frontier of solutions to problems of robust design in multiobjective systems. The PGA was designed to be applied using Taguchi’s Parameter Design method, which is the most frequently used approach by practitioners to executing robust design studies. We tested the PGA using data obtained from a real singleoutput system and from multiobjective process simulators with many control and noise factors. In all cases, the PGA delivered Pareto-optimal solutions that adequately achieved the objective of robust design. Additionally, the discussion of the results showed that having those Pareto solutions helps in the selection of the best ones to be implemented in the system under study, especially when the system has many control factors and responses.
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H. Allende, E. Canessa, J. Galbiati. Diseño de Experimentos Industriales. 1st ed. Ed. Universidad Técnica Federico Santa María. Valparaíso, Chile. 2005. pp. 27-200.
G. Taguchi. Systems of experimental design. 4th ed. Ed. American Supplier Institute. Dearborn, USA. 1991. pp. 397-511.
G. Alexander. How to (almost) schedule innovation. Research Technology Management. Ed. Industrial Research Institute. 2002. pp. 31-40. DOI: https://doi.org/10.1080/08956308.2002.11671480
D. Drickhamer. “BASF breaks through with statistics”. Industry Week. June 2002. pp. 81-82
C. Simms, J. Garvin. “It’s a black art: design of experiments switches on the light”. Managerial Auditing Journal. Vol. 17. 2002. pp. 65-71. DOI: https://doi.org/10.1108/02686900210412261
R. Roy. Design of Experiments Using the Taguchi Approach. 1st ed. Ed. J. Wiley & Sons. New York, USA. 2001. pp. 8-513.
J. Cesarone. “The power of Taguchi”. IIE Solutions. November 2001. pp. 36-40.
J. Antony, F. Antony. “Teaching the Taguchi method to industrial engineers”. Work Study. Vol. 50. 2001. pp. 141-149. DOI: https://doi.org/10.1108/00438020110391873
K. Ballantyne, R. van Oorschot, R. Mitchell. “Reduce optimisation time and effort: Taguchi experimental design methods”. Forensic Science International: Genetics Supplement Series. Vol. 1. 2008, pp. 7-8. DOI: https://doi.org/10.1016/j.fsigss.2007.10.050
S. Maghsoodloo, G. Ozdemir, V. Jordan, Ch. Huang. “Strengths and limitations of Taguchi’s contributions to quality, manufacturing, and process engineering”. Journal of Manufacturing Systems. Vol. 23. 2004. pp. 73-126. DOI: https://doi.org/10.1016/S0278-6125(05)00004-X
H. Allende, D. Bravo, E. Canessa. “Robust design in multivariate systems using genetic algorithms”. Quality & Quantity Journal. Vol. 44. 2010. pp. 315-332. DOI: https://doi.org/10.1007/s11135-008-9201-z
S. Maghsoodloo, C. Chang. “Quadratic loss functions and SNR for a bivariate response”. Journal of Manufacturing Systems. Vol. 20. 2001. pp. 1-12. DOI: https://doi.org/10.1016/S0278-6125(01)80015-7
E. Canessa, C. Droop, H. Allende. “An improved genetic algorithm for robust design in multivariate systems”. Quality & Quantity Journal. Vol. 42. 2011. pp. 665-678. DOI: https://doi.org/10.1007/s11135-010-9420-y
C. Fonseca, P. Flemming. Genetic algorithms for multiobjective optimization: Formulation, discussion, and generalization. Proceedings of 5th International Conference on Genetic Algorithms, Morgan Kaufmann. San Francisco, USA. 1993. pp. 416-423.
A. Kumar, A. Keane, P. Nair, S. Shahpar. “Efficient genetic algorithm based robust design method for compressor fan blades”. Proceedings of ASME IDETC/CIE Conferences on Design Engineering. Long Beach, USA. 2005. pp. 1-9. DOI: https://doi.org/10.1115/DETC2005-85042
A. Hajiloo, N. Nariman-Zadeh, A. Moeini. “Pareto optimal robust design of fractional-order PID controllers for systems with probabilistic uncertainties”. Mechatronics. Vol. 22. 2012. pp. 788–801. Available on: http://dx.doi.org/10.1016/j. mechatronics.2012.04.003. Accessed: May 21, 2012. DOI: https://doi.org/10.1016/j.mechatronics.2012.04.003
A. Jamali, A. Hajiloo, N. Nariman-zadeh. 2010. “Reliability-based robust Pareto design of linear state feedback controllers using a multi-objective uniform-diversity genetic algorithm (MUGA)”. Expert Systems with Applications. Vol. 37. 2010. pp. 401-413. DOI: https://doi.org/10.1016/j.eswa.2009.05.048
O. Köksoy, T. Yalcinoz. “Robust design using Pareto type optimization: a genetic algorithm with arithmetic crossover”. Computers & Industrial Engineering. Vol. 55. 2008. pp. 208-218. DOI: https://doi.org/10.1016/j.cie.2007.11.019
B. Forouraghi. “A Genetic Algorithm for Multiobjective Robust Design”. Applied Intelligence. Vol. 12. 2000. pp. 151-161. DOI: https://doi.org/10.1023/A:1008356321921
K. Hacker, K. Lewis. Robust design through the use of a hybrid genetic algorithm. Proceedings of ASME IDETC/CIE Conferences on Design Engineering. Montreal, Canada. 2002. pp. 1-10. DOI: https://doi.org/10.1115/DETC2002/DAC-34108
N. Yusoff, M. Ramasamy, S. Yusup. “Taguchi’s parametric design approach for the selection of optimization variables in a refrigerated gas plant”. Chemical Engineering Research and Design. Vol. 89. 2011. pp. 665-675. DOI: https://doi.org/10.1016/j.cherd.2010.09.021
J. Zhang, J. Chen, E. Kirby. “Surface roughness optimization in an end-milling operation using the Taguchi design method”. Journal of Materials Processing Technology. Vol. 184. 2007. pp. 233-239. DOI: https://doi.org/10.1016/j.jmatprotec.2006.11.029
E. Del Castillo, D. Montgomery, D. McCarville. “Modified desirability functions for multiple response optimization”. Journal of Quality Technology. Vol. 28. 1996. pp. 337-345. DOI: https://doi.org/10.1080/00224065.1996.11979684
F. Ortiz, J. Simpson, J. Pigniatiello, A. Heredia–Langner. “A Genetic Algorithm Approach to Multiple–Response Optimization”. Journal of Quality Technology. Vol. 36. 2004. pp. 432- 449. DOI: https://doi.org/10.1080/00224065.2004.11980289
M. Laumanns, L. Thiele, E. Zitzler. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103. Computer Engineering and Networks Laboratory (TIK). Swiss Federal Institute of Technology (ETH). Zurich, Switzerland. 2001. pp. 1-21.
M. Laumanns. SPEA2-The Strength Pareto Evolutionary Algorithm 2. Swiss Federal Institute of Technology (ETH). Zurich, Switzerland. 2008. Available on: http://www.tik.ee.ethz.ch/~sop/pisa/selectors/spea2/?page=spea2.php. Accessed: October 10, 2012.
W. Vandenbrande. Make love, not war: Combining DOE and Taguchi. ASQ´s 54th Annual Quality Congress Proceedings. Indianapolis, USA. 2000. pp. 450-456.
C. Coello, G. Lamont, D. Van Veldhuizen, D. Evolutionary Algorithms for solving Multi-Objective Problems. 2nd ed. Ed. Springer Science+Business Media. New York, USA. 2007. pp. 233-282.
R. Myers, D. Montgomery. Response surface methodology: process and product optimization using designed experiments. 2nd ed. Ed. J. Wiley & Sons. New York, USA. 2002. pp. 536-605.
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