Technology selection in the presence of imprecise data, weight restrictions, and nondiscretionary factors

  • Reza Farzipoor SaenEmail author


Technology selection is an important part of management of technology. Traditionally, technology selection models are based on cardinal data with less emphasis on ordinal data. However, with respect to technology selection complexity, emphasis has shifted to the simultaneous consideration of cardinal and ordinal data in technology selection process. The application of data envelopment analysis (DEA) for technology selection problems is based on total flexibility of the weights. However, the problem of allowing total flexibility of the weights is that the values of the weights obtained by solving the unrestricted DEA program are often in contradiction to prior views or additional available information. On the other hand, current models of technology selection problems assume complete discretion of decision-making criteria and do not assume technology selection in the conditions that some factors are nondiscretionary. To select the best technologies in the presence of cardinal data, ordinal data, nondiscretionary factors, and weight restrictions, the objective of this paper is to propose a new pair of assurance region-nondiscretionary factors-imprecise data envelopment analysis (AR-NF-IDEA) models. A numerical example demonstrates the application of the proposed method.


Technology selection Imprecise data envelopment analysis Nondiscretionary factors Assurance region 


  1. 1.
    Rai R, Kameshwaran S, Tiwari MK (2002) Machine-tool selection and operation allocation in FMS: Solving a fuzzy goal-programming model using a genetic algorithm. Int J Prod Res 40(3):641–665 DOI  10.1080/00207540110081515 zbMATHCrossRefGoogle Scholar
  2. 2.
    Chan FTS, Swarnkar R, Tiwari MK (2005) Fuzzy goal-programming model with an artificial immune system (AIS) Approach for a machine tool selection and operation allocation problem in a flexible manufacturing system. Int J Prod Res 43(19):4147–4163 DOI  10.1080/00207540500140823 zbMATHCrossRefGoogle Scholar
  3. 3.
    Jaganathan S, Erinjeri JJ, Ker J (2007) Fuzzy analytic hierarchy process based group decision support system to select and evaluate new manufacturing technologies. Int J Adv Manuf Technol 32(11–12):1253–1262 DOI  10.1007/s00170-006–0446-1 CrossRefGoogle Scholar
  4. 4.
    Khouja M (1995) The use of data envelopment analysis for technology selection. Comput Ind Eng 28(1):123–132 DOI  10.1016/0360-8352(94)00032-I CrossRefGoogle Scholar
  5. 5.
    Baker RC, Talluri S (1997) A closer look at the use of data envelopment analysis for technology selection. Comput Ind Eng 32(1):101–108 DOI  10.1016/S0360-8352(96)00199-4 CrossRefGoogle Scholar
  6. 6.
    Ramanathan R (2001) Comparative risk assessment of energy supply technologies: A data envelopment analysis approach. Energy 26(2):197–203 DOI  10.1016/S0360–5442(00)00058-X CrossRefMathSciNetGoogle Scholar
  7. 7.
    Farzipoor SR (2006) A decision model for technology selection in the existence of both cardinal and ordinal data. Appl Math Comput 181(2):1600–1608 DOI  10.1016/j.amc.2006.03.012 zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Talluri S, Whiteside MM, Seipel SJ (2000) A nonparametric stochastic procedure for FMS Evaluation. Eur J Oper Res 124(3):529–538 DOI  10.1016/S0377-2217(99)00188-5 zbMATHCrossRefGoogle Scholar
  9. 9.
    Seiford LM, Zhu J (2003) Context-dependent data envelopment analysis-measuring attractiveness and progress. Omega 31(5):397–408 DOI  10.1016/S0305-0483(03)00080-X CrossRefGoogle Scholar
  10. 10.
    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(8):1537–1554 DOI  10.1080/13528160412331326478 zbMATHCrossRefGoogle Scholar
  11. 11.
    Sarkis J, Talluri S (1999) A decision model for evaluation of flexible manufacturing systems in the presence of both cardinal and ordinal factors. Int J Prod Res 37(13):2927–2938 DOI  10.1080/002075499190356 zbMATHCrossRefGoogle Scholar
  12. 12.
    Cook WD, Kress M, Seiford LM (1996) Data envelopment analysis in the presence of both quantitative and qualitative factors. J Oper Res Soc 47(7):945–953zbMATHCrossRefGoogle Scholar
  13. 13.
    Talluri S, Yoon KP (2000) A cone-ratio DEA approach for AMT justification. Int J Prod Econ 66(2):119–129 DOI  10.1016/S0925-5273(99)00123–1 CrossRefGoogle Scholar
  14. 14.
    Shang J, Sueyoshi T (1995) A unified framework for the selection of flexible manufacturing System. Eur J Oper Res 85(2):297–315 DOI  10.1016/0377–2217(94)00041-A zbMATHCrossRefGoogle Scholar
  15. 15.
    Braglia M, Petroni A (1999) Evaluating and selecting investments in industrial robots. Int J Prod Res 37(18):4157–4178 DOI  10.1080/002075499189718 zbMATHCrossRefGoogle Scholar
  16. 16.
    Farzipoor SR (2006) A decision model for selecting technology suppliers in the presence of nondiscretionary factors. Appl Math Comput 181(2):1609–1615 DOI  10.1016/j.amc.2006.03.013 zbMATHCrossRefGoogle Scholar
  17. 17.
    Farzipoor SR (2006) An algorithm for ranking technology suppliers in the presence of nondiscretionary factors. Appl Math Comput 181(2):1616–1623 DOI  10.1016/j.amc.2006.03.014 zbMATHCrossRefGoogle Scholar
  18. 18.
    Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444 DOI  10.1016/0377–2217(78)90138–8 zbMATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Banker RD, Charnes A, Cooper WW (1984) Some methods for estimating technical and scale inefficiencies in data envelopment analysis. Manage Sci 30(9):1078–1092zbMATHCrossRefGoogle Scholar
  20. 20.
    Charnes A, Cooper WW, Wei QL, Huang ZM (1989) Cone-ratio data envelopment analysis and multi-objective programming. Int J Syst Sci 20(7):1099–1118 DOI  10.1080/00207728908910197 zbMATHCrossRefMathSciNetGoogle Scholar
  21. 21.
    Thompson RG, Langemeier LN, Lee CT, Lee E, Thrall RM (1990) The role of multiplier bounds in efficiency analysis with application to kansas farming. J Econom 46(1/2):93–108 DOI  10.1016/0304-4076(90)90049-Y CrossRefGoogle Scholar
  22. 22.
    Wong YHB, Beasley JE (1990) Restricting weight flexibility in data envelopment analysis. J Oper Res Soc 41(9):829–835zbMATHCrossRefGoogle Scholar
  23. 23.
    Sarrico CS, Dyson RG (2004) Restricting virtual weights in data envelopment analysis. Eur J Oper Res 159(1):17–34 DOI  10.1016/S0377–2217(03)00402–8 zbMATHCrossRefMathSciNetGoogle Scholar
  24. 24.
    Cooper WW, Park KS, Yu G (1999) IDEA and AR-IDEA: Models for dealing with imprecise data in DEA. Manage Sci 45(4):597–607CrossRefGoogle Scholar
  25. 25.
    Cooper WW, Park KS, Yu G (2001) An illustrative application of IDEA (imprecise data envelopment analysis) to a Korean mobile telecommunication company. Oper Res 49(6):807–820 DOI  10.1287/opre.49.6.807.10022 CrossRefzbMATHGoogle Scholar
  26. 26.
    Cooper WW, Park KS, Yu G (2001) IDEA (Imprecise Data Envelopment Analysis) with CMDs (Column Maximum Decision Making Units). J Oper Res Soc 52(2):176–181 DOI  10.1057/palgrave.jors.2601070 zbMATHCrossRefGoogle Scholar
  27. 27.
    Wang YM, Greatbanks R, Yang JB (2005) Interval efficiency assessment using data envelopment analysis. Fuzzy Sets Syst 153(3):347–370zbMATHMathSciNetGoogle Scholar
  28. 28.
    Despotis DK, Smirlis YG (2002) Data envelopment analysis with imprecise data. Eur J Oper Res 140(1):24–36 DOI  10.1016/S0377-2217(01)00200-4 zbMATHCrossRefMathSciNetGoogle Scholar
  29. 29.
    Zhu J (2003) Imprecise data envelopment analysis (IDEA): A review and improvement with an application. Eur J Oper Res 144(3):513–529 DOI  10.1016/S0377-2217(01)00392-7 zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  1. 1.Department of Industrial Management, Faculty of Management and AccountingIslamic Azad University-Karaj BranchKarajIran

Personalised recommendations