Data envelopment analysis and Pareto genetic algorithm applied to robust design in multiresponse systems

Authors

DOI:

https://doi.org/10.17533/udea.redin.n79a11

Keywords:

robust design, Taguchi methods, Pareto genetic algorithms, data envelopment analysis

Abstract


This paper shows the use of Data Envelopment Analysis (DEA) to rank and select the solutions found by a Pareto Genetic Algorithm (PGA) to problems of robust design in multiresponse systems with many control and noise factors. The efficiency analysis of the solutions using DEA shows that the PGA finds a good approximation to the efficient frontier. Additionally, DEA is used to determine the combination of a given level of mean adjustment and variance in the responses of a system, so as to minimize the economic cost of achieving those two objectives. By linking that cost with other technical and/or economic considerations, the solution that best matches a predefined level of quality can be more sensibly selected.

|Abstract
= 57 veces | PDF
= 99 veces|

Downloads

Download data is not yet available.

Author Biographies

Enrique Carlos Canessa Tenazas, Universidad Adolfo Ibáñez

Faculty of Engineering and Sciences

Filadelfo De Mateo Gómez, Universidad de Valparaíso

School of Industrial Engineering, Faculty of Engineering

Wilfredo Fernando Yushimito Del Valle, Universidad Adolfo Ibáñez

Faculty of Engineering and Sciences

References

G. Taguchi, Systems of experimental design, 4th ed. Dearborn, USA: American Supplier Institute, 1991.

T. Robinson, C Borror and R. Myers, “Robust Parameter Design: A Review”, Quality & Reliability Engineering Internation, vol. 20, no. 1, pp. 81-101, 2004.

H. Allende, E. Canessa and J. Galbiati, Diseño de experimentos industriales, 1st ed. Valparaíso, Chile: Universidad Técnica Federico Santa María, 2005.

H. Allende, D. Bravo and E. Canessa, “Robust design in multivariate systems using genetic algorithms”, Quality & Quantity, vol. 44, no. 2, pp. 315-332, 2010.

S. Maghsoodloo and C. Chang, “Quadratic loss functions and signal-to-noise ratios for a bivariate response”, Journal of Manufacturing Systems, vol. 20, no. 1, pp. 1-12, 2001.

W. Wan and J. Birch, “Using a modified genetic algorithm to find feasible regions of a desirability function”, Quality & Reliability Engineering International, vol. 27, no. 8, pp. 1173-1182, 2011.

J. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, 1st ed. Ann Arbor, USA: MIT Press, 1974.

C. Lin, C. Anderson, M. Hamada, L. Moore and R. Sitter, “Using Genetic Algorithms to Design Experiments: A Review”, Quality & Reliability Engineering International, vol. 31, no. 2, pp. 155-167, 2015.

B. Forouraghi, “A Genetic Algorithm for Multiobjective Robust Design”, Applied Intelligence, vol. 12, no. 3, pp. 151-161, 2000.

E. Canessa, G. Bielenberg and H. Allende, “Robust Design in Multiobjective Systems using Taguchi’s Parameter Design Approach and a Pareto Genetic Algorithm”, Rev. Fac. Ingeniería Univ. Antioquia, no. 72, pp. 73-86, 2014.

A. Charnes, W. Cooper and E. Rhodes, “Measuring the Efficiency of Decision Making Units”, European Journal of Operational Research, vol. 2, no. 6, pp. 429-444, 1978.

D. Aigner, C. Lovell and P. Schmidt, “Formulation and Estimation of Stochastic Frontier Production Function Models”, Journal of Econometrics, vol. 6, no. 1, pp. 21- 37, 1977.

F. Ortiz, J. Simpson, J. Pigniatello and A. Heredia, “A Genetic Algorithm Approach to Multiple - Response Optimization”, Journal of Quality Technology, vol. 36, no. 4, pp. 432-450, 2004.

E. Castillo, D. Montgomery and D. McCarville, “Modified Desirability Functions for Multiple Response Optimization”, Journal of Quality Technology, vol. 28, no. 3, pp. 337- 345, 1996.

M. Farrell, “The Measurement of Productive Efficiency”, Journal of the Royal Statistical Society Series A General, vol. 120, no. 3, pp. 253-290, 1957.

W. Cooper, L. Seiford and K. Tone, Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software, 2nd ed. New York, USA: Springer, 2007.

R. Färe, S. Groskopf, C. Lovell, The Measurement of Efficiency of Production, 1st ed. Boston, USA: KluwerNijhoff Publishing Co., 1985.

E. Canessa, C. Droop and H. Allende, “An improved genetic algorithm for robust design in multivariate systems”, Quality & Quantity, vol. 46, no. 2, pp. 665-678, 2011.

W. Vandenbrande, “Make love, not war: Combining DOE and Taguchi”, in ASQ´s 54th Annual Quality Congress Proceedings, Indianapolis, USA, 2000, pp. 450-456.

W. Vandenbrande, “SPC in paint application: Mission Impossible?”, in ASQ’s 52nd Annual Quality Congress Proceedings, Indianapolis, USA, 1998, pp. 708-715.

K. Ranjit, Design of Experiments Using the Taguchi Approach: 16 Steps to Product and Process Improvement, 1st ed. New York, USA: J. Wiley & Sons, 2001.

K. Deb, L. Thiele and E. Zitzler, “Comparison of Multiobjetive Evolutionary Algorithms: Empirical Results”, Evolutionary Computation, vol. 8, no. 2, pp. 173-195, 2000.

R. Myers, D. Montgomery and Christine M. Anderson, Response surface methodology: process and product optimization using designed experiments, 2nd ed. New York, USA: J. Wiley & Sons, 2002.

Downloads

Published

2016-06-16

How to Cite

Canessa Tenazas, E. C., De Mateo Gómez, F., & Yushimito Del Valle, W. F. (2016). Data envelopment analysis and Pareto genetic algorithm applied to robust design in multiresponse systems. Revista Facultad De Ingeniería Universidad De Antioquia, (79), 119–129. https://doi.org/10.17533/udea.redin.n79a11