Skip to main content
Log in

A new DEA model for technology selection in the presence of ordinal data

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

This paper suggests a data envelopment analysis (DEA) model for selecting the most efficient alternative in advanced manufacturing technology in the presence of both cardinal and ordinal data. The paper explains the problem of using an iterative method for finding the most efficient alternative and proposes a new DEA model without the need of solving a series of LPs. A numerical example illustrates the model, and an application in technology selection with multi-inputs/multi-outputs shows the usefulness of the proposed approach.

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

Access this article

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

  1. Amin GR, Toloo M, Sohrabi B (2006) An improved MCDM DEA model for technology selection. Int J Prod Res 44:2681–2686

    Article  MATH  Google Scholar 

  2. Amin GR, Emrouznejad A (2007) A note on DEA models in technology selection: an improvement of Karsak and Ahiska's approach. Int J Prod Res 45:2313–2316

    Article  Google Scholar 

  3. Amin GR (2008) A note on an improved MCDM DEA model for technology selection. Int J Prod Res 46(24):7073–7075

    Article  Google Scholar 

  4. Karsak EE, Ahiska SS (2005) Practical common weight multi-criteria decision-making approach with an improved discriminating power for technology selection. Int J Prod Res 43:1537–1554

    Article  MATH  Google Scholar 

  5. Karsak EE, Ahiska SS (2008) Improved common weight MCDM model for technology selection. Int J Prod Res 46(24):6933–6944

    Article  Google Scholar 

  6. Talluri S, Yoon KP (2000) A cone-ratio DEA approach for AMT justification. Int J Prod Econ 66:119–129

    Article  Google Scholar 

  7. Farzipoor Saen R (2009) Technology selection in the presence of imprecise data, weight restrictions, and nondiscretionary factors. Int J Adv Manuf Technol 41(7–8):827–838

    Article  Google Scholar 

  8. Braglia M, Petroni A (1999) Evaluating and selecting investments in industrial robots. Int J Prod Res 37:4157–4178

    Article  MATH  Google Scholar 

  9. Cook WD, Kress M, Seiford LM (1996) Data envelopment analysis in the presence of both quantitative and qualitative factors. J Oper Res Soc 47:945–953

    MATH  Google Scholar 

  10. Karsak EE (2008) Using data envelopment analysis for evaluating flexible manufacturing systems in the presence of imprecise data. Int J Adv Manuf Technol 35(9–10):867–874

    Article  Google Scholar 

  11. Ertay T, Ruan D, Tuzkaya UR (2006) Integrating data envelopment analysis and analytic hierarchy for the facility layout design in manufacturing systems. Info Sci 176:237–262

    Article  Google Scholar 

  12. Wang YM, Chin KS (2009) A new approach for the selection of advanced manufacturing technologies: DEA with double frontiers. Int J Prod Res 47(23):6663–6679

    Article  MATH  Google Scholar 

  13. Azadeh A, Anvari M, Ziaei B, Sadeghi K (2010) An integrated fuzzy DEA–fuzzy C-means–simulation for optimization of operator allocation in cellular manufacturing systems. Int J Adv Manuf Technol 46(1–4):361–375

    Article  Google Scholar 

  14. Chuu SJ (2009) Selecting the advanced manufacturing technology using fuzzy multiple attributes group decision making with multiple fuzzy information. Comput Ind Eng 57(3):1033–1042

    Article  Google Scholar 

  15. Amin GR, Toloo M, Sheikhan M (2010) Inputs and outputs scaling in advanced manufacturing technology: theory and application. Int J Adv Manuf Technol 50(9–12):1235–1241

    Google Scholar 

  16. Cooper WW, Seiford LM, Tone K (2006) Introduction to data envelopment analysis and its uses with DEA-solver software and references. Springer, New York

    Google Scholar 

  17. Kahraman C, Kaya I, Çevik S, Ates NY, Gülbay M (2008) Fuzzy multi-criteria evaluation of industrial robot systems using TOPSIS. In: Kahraman C (ed) Fuzzy multi-criteria decision making, Springer Optimization and Its Applications, 2008, Volume 16, I, 159–186

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gholam R. Amin.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Amin, G.R., Emrouznejad, A. A new DEA model for technology selection in the presence of ordinal data. Int J Adv Manuf Technol 65, 1567–1572 (2013). https://doi.org/10.1007/s00170-012-4280-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-012-4280-3

Keywords

Navigation