Skip to main content

Advertisement

Log in

Solving the multi-response problem in Taguchi method by benevolent formulation in DEA

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

The Taguchi method is an efficient approach for optimizing a single quality response. In practice, however, most products/processes have more than one quality response of main interest. Recently, the multi-response problem in the Taguchi method has gained a considerable research attention. This research, therefore, proposes an efficient approach for solving the multi-response problem in the Taguchi method utilizing benevolent formulation in data envelopment analysis (DEA). Each experiment in Taguchi’s orthogonal array (OA) is treated as a decision making unit (DMU) with multiple responses set inputs and/or outputs. Each DMU is evaluated by benevolent formulation. The ordinal value of the DUM’s efficiency is then used to decide the optimal factor levels for multi-response problem. Three frequently-investigated case studies are adopted to illustrate the proposed approach. The computational results showed that the proposed approach provides the largest total anticipated improvement among principal component analysis (PCA), DEA based ranking approach (DEAR) and other techniques in literature. In conclusion, the proposed approach may provide a great assistant to practitioners for solving the multi-response problem in manufacturing applications on the Taguchi method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Al-Refaie A., Li M. H. C., Tai K. C. (2008) Optimizing SUS 304 wire drawing process by grey analysis utilizing Taguchi method. Journal of University of Science and Technology Beijing 15(6): 714–722

    Google Scholar 

  • Anastasiou K.S. (2002) Optimization of the aluminium die casting process based on the Taguchi method. Proceedings of the Institution of Mechanical Engineers, 216(7): 969–977

    Article  Google Scholar 

  • Angulo-Meza L., Lins M. P. E. (2002) Review of methods for increasing discrimination in data envelopment analysis. Annuals of Operations Research 116: 225–242

    Article  Google Scholar 

  • Antony J. (2000) Multi-response optimization in industrial experiments using Taguchi’s quality loss function and principal component analysis. Quality and Reliability Engineering International 16: 3–8

    Article  Google Scholar 

  • Baker R. C., Talluri S. (1997) A closer look at the use of data envelopment analysis for technology selection. Computers and Industrial Engineering 28: 101–108

    Article  Google Scholar 

  • Charnes A., Cooper W. W., Huang Z. M., Sun D. B. (1990) Polyhedral cone-ratio DEA models with an illustrative application to large commercial banks. Journal of Econometrics 46: 73–91

    Article  Google Scholar 

  • Charnes A., Cooper W. W., Letwin A. A., Seiford L. M. (1994) Data envelopment analysis. Kluwer, Dordrecht

    Google Scholar 

  • Charnes A., Cooper W. W., Rhodes E. (1978) Measuring the efficiency of decision making units. European Journal of Operational Research 2: 429–444

    Article  Google Scholar 

  • Dyson R. G., Thanassoulis E. (1988) Reducing weight flexibility in data envelopment analysis. Journal of the Operational Research Society 39: 563–576

    Google Scholar 

  • Halme M., Joro T., Korhonen P., Salo S., Wallenius J. (2000) Value efficiency analysis for incorporating preference information in DEA. Management Science 45: 103–115

    Article  Google Scholar 

  • Jeyapaul R., Shahabudeen P., Krishnaiah K. (2006) Simultaneous optimization of multi-response problems in the Taguchi method using genetic algorithm. International Journal of Advanced Manufacturing Technology 30: 870–878

    Article  Google Scholar 

  • Khouja M. (1995) The use of data envelopment analysis for technology selection. Computers and Industrial Engineering 28: 123–132

    Article  Google Scholar 

  • Li M. H, Al-Refaie A., Yang C. Y. (2008) DMAIC approach to improve the capability of SMT solder printing process. IEEE Transactions on Electronics Packaging Manufacturing 24: 351–360

    Google Scholar 

  • Liao H. C. (2005) Using N-D method to solve multi-response problem in Taguchi. Journal of Intelligent Manufacturing 16: 331–347

    Article  Google Scholar 

  • Liao H. C., Chen Y. K. (2002) Optimizing multi-response problem in the Taguchi method by DEA based ranking method. International Journal of Quality and Reliability Management 19(7): 825–837

    Article  Google Scholar 

  • Phadke M. S. (1989) Quality engineering using robust design. Prentice-Hall, Englewood Cliffs, NJ

    Google Scholar 

  • Reddy P. B. S., Nishina K., Subash Babu A. (1997) Unification of robust design and goal programming for multiresponse optimization—a case study. Quality and Reliability Engineering International 13: 371–383

    Article  Google Scholar 

  • Sexton, T. R., Slinkman, R. H., & Hogan, A. (1986). Data envelopment analysis: Critique and extensions. In R. H. Slinkman (Ed.), Measuring efficiency: An assessment of data envelopment analysis (vol. 32). New directions of program evaluation, Jossey Bass, San Francisco.

  • Shiau G. H. (1990) A study of the sintering properties of iron ores using the Taguchi’s parameter design. Journal of the Chinese Statistical Association 28: 253–275

    Google Scholar 

  • Su C. T., Tong L. I. (1997) Multi-response robust design by principal component analysis. Total Quality Management 8(6): 409–416

    Article  Google Scholar 

  • Taguchi G. (1991) Taguchi methods. Research and development (Vol. 1). American Suppliers Institute Press, Dearborn, MI

    Google Scholar 

  • Tong L. I., Su C. T., Wang C. H. (1997) The optimization of multi-response problems in the Taguchi method. International Journal of Quality and Reliability Management 14(4): 367–380

    Article  Google Scholar 

  • Tsao C. C., Hocheng H. (2002) Comparison of the tool life of tungsten carbides coated by multi-layer TiCN and TiAlCN for end mills using the Taguchi method. Journal of Material Processing Technology 123: 1–4

    Article  Google Scholar 

  • Yu J. C., Chen X. X., Hung T. R., Thibault F. (2004) Optimization of extrusion blow molding processes using soft computing and Taguchi’s method. Journal of Intelligent Manufacturing 15: 625–634

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Abbas Al-Refaie.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Al-Refaie, A., Al-Tahat, M.D. Solving the multi-response problem in Taguchi method by benevolent formulation in DEA. J Intell Manuf 22, 505–521 (2011). https://doi.org/10.1007/s10845-009-0312-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-009-0312-8

Keywords

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy