249
Views
12
CrossRef citations to date
0
Altmetric
Original Articles

Efficiency bounds and efficiency classifications in imprecise DEA: An extension

ORCID Icon & ORCID Icon
Pages 491-504 | Received 12 Jun 2018, Accepted 17 Dec 2018, Published online: 20 Apr 2019

Reference

  • Arnold, V., Bardhan, I. R., & Cooper, W. W. (1997). A two-stage DEA approach for identifying secondary, and rewarding efficiency in Texas. In W. W. Cooper, S. Thore, D. Gibson, & F. PhiUips (Eds.), Impact: How IC2 institute research affects public policy and business practices. USA: Praeger.
  • Asosheh, A., Nalchigar, S., & Jamporazmey, M. (2010). Information technology project evaluation: An integrated data envelopment analysis and balanced scorecard approach. Expert Systems with Applications, 37(8), 5931–5938. doi:10.1016/j.eswa.2010.02.012
  • Baghery, M., Yousefi, S., & Mustafa, J. R. (2016). Risk measurement and prioritization of auto parts manufacturing processes based on process failure analysis, interval data envelopment analysis and grey relational analysis. Journal of Intelligent Manufacturing, 29(8), 1–23.
  • Bazaraa, M. S., Jarvis, J. J., & Sherali, H. D. (2010). Linear programming and network flows (4th ed.). Hoboken, NJ: John Wiley & Sons.
  • Charnes, A., & Cooper, W. W. (1962). Programming with linear fractional functionals. Naval Research Logistics Quarterly, 9(3–4), 181–186. doi:10.1002/nav.3800090303
  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. doi:10.1016/0377-2217(78)90138-8
  • Charnes, A., Rousseau, J., & Semple, J. (1993). An effective non-Archimedean anti-degeneracy/cycling linear programming method especially for data envelopment analysis and like models. Annals of Operations Research, 46–47(2), 271–278. doi:10.1007/BF02023099
  • Chen, Y., Cook, W. D., Du, J., Hu, H., & Zhu, J. (2017). Bounded and discrete data and Likert scales in data envelopment analysis: Application to regional energy efficiency in China. Annals of Operations Research, 255(1–2), 347–366. doi:10.1007/s10479-015-1827-3
  • Cooper, W. W., Park, K. S., & Yu, G. (1999). IDEA and AR-IDEA: Models for dealing with imprecise data in DEA. Management Science, 45(4), 597–607. doi:10.1287/mnsc.45.4.597
  • Cooper, W. W., Park, K. S., & Yu, G. (2001). IDEA (Imprecise Data Envelopment Analysis) with CMDs (Column Maximum Decision Making Units). Journal of the Operational Research Society, 52(2), 176–181. doi:10.1057/palgrave.jors.2601070
  • Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-solver software (2nd ed.). NY, USA: Springer US.
  • Despotis, D. K., & Smirlis, Y. G. (2002). Data envelopment analysis with imprecise data. European Journal of Operational Research, 140(1), 24–36. doi:10.1016/S0377-2217(01)00200-4
  • Ebrahimi, B., Rahmani, M., & Ghodsypour, S. H. (2017). A new simulation-based genetic algorithm to efficiency measure in IDEA with weight restrictions. Measurement, 108, 26–33. doi:10.1016/j.measurement.2017.05.026
  • Farzipoor Saen, R. (2007). Suppliers selection in the presence of both cardinal and ordinal data. European Journal of Operational Research, 183(2), 741–747. doi:10.1016/j.ejor.2006.10.022
  • Hatami-Marbini, A., Emrouznejad, A., & Agrell, P. J. (2014). Interval data without sign restrictions in DEA. Applied Mathematical Modelling, 38(7–8), 2028–2036. doi:10.1016/j.apm.2013.10.027
  • He, F., Xu, X., Chen, R., & Zhu, L. (2016). Interval efficiency improvement in DEA by using ideal points. Measurement: Journal of the International Measurement Confederation, 87, 138–145. doi:10.1016/j.measurement.2016.02.062
  • Kao, C. (2006). Interval efficiency measures in data envelopment analysis with imprecise data. European Journal of Operational Research, 174(2), 1087–1099. doi:10.1016/j.ejor.2005.03.009
  • Karsak, E. E., & Dursun, M. (2014). An integrated supplier selection methodology incorporating QFD and DEA with imprecise data. Expert Systems with Applications, 41(16), 6995–7004. doi:10.1016/j.eswa.2014.06.020
  • Khalili-Damghani, K., Tavana, M., & Haji-Saami, E. (2015). A data envelopment analysis model with interval data and undesirable output for combined cycle power plant performance assessment. Expert Systems with Applications, 42(2), 760–773. doi:10.1016/j.eswa.2014.08.028
  • Kim, S.-H., Park, C.-G., & Park, K. S. (1999). An application of data envelopment analysis in telephone offices evaluation with partial data. Computers & Operations Research, 26(1), 59–72. doi:10.1016/S0305-0548(98)00041-0
  • Lee, Y. K., Sam Park, K., & Kim, S. H. (2002). Identification of inefficiencies in an additive model based IDEA (imprecise data envelopment analysis). Computers & Operations Research, 29(12), 1661–1676. doi:10.1016/S0305-0548(01)00049-1
  • Park, K. S. (2004). Simplification of the transformations and redundancy of assurance regions in IDEA (imprecise DEA). Journal of the Operational Research Society, 55(12), 1363–1366. doi:10.1057/palgrave.jors.2601824
  • Park, K. S. (2007). Efficiency bounds and efficiency classifications in DEA with imprecise data. Journal of the Operational Research Society, 58(4), 533–540. doi:10.1057/palgrave.jors.2602178
  • Park, K. S. (2010). Duality, efficiency computations and interpretations in imprecise DEA. European Journal of Operational Research, 200(1), 289–296. doi:10.1016/j.ejor.2008.11.028
  • Podinovski, V. V. (2004). Production trade-offs and weight restrictions in data envelopment analysis. Journal of the Operational Research Society, 55(12), 1311–1322. doi:10.1057/palgrave.jors.2601794
  • Podinovski, V. V., & Bouzdine-Chameeva, T. (2013). Weight restrictions and free production in data envelopment analysis. Operations Research, 61(2), 426–437. doi:10.1287/opre.1120.1122
  • Podinovski, V. V., & Bouzdine-Chameeva, T. (2017). Solving DEA models in a single optimization stage: Can the non-Archimedean infinitesimal be replaced by a small finite epsilon? European Journal of Operational Research, 257(2), 412–414.
  • Salahi, M., & Toloo, M. (2017). In the determination of the most efficient decision making unit in data envelopment analysis: A comment. Computers and Industrial Engineering, 104, 216–218. doi:10.1016/j.cie.2016.12.032
  • Soyster, A. L. (1973). Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations Research, 21(5), 1154–1157. doi:10.1287/opre.21.5.1154
  • Toloo, M. (2014a). Selecting and full ranking suppliers with imprecise data: A new DEA method. The International Journal of Advanced Manufacturing Technology, 74(5–8), 1141–1148. doi:10.1007/s00170-014-6035-9
  • Toloo, M. (2014b). The role of non-Archimedean epsilon in finding the most efficient unit: With an application of professional tennis players. Applied Mathematical Modelling, 38(21–22), 5334–5346. doi:10.1016/j.apm.2014.04.010
  • Toloo, M. (2015). A technical note on “erratum to Finding the most efficient DMUs in DEA: An improved integrated model [Comput. Indus. Eng. 52 (2007) 71-77].”. Computers and Industrial Engineering, 83, 261–263.
  • Toloo, M., Keshavarz, E., & Hatami-Marbini, A. (2018). Dual-role factors for imprecise data envelopment analysis. Omega, 77, 15–31. doi:10.1016/j.omega.2017.05.005
  • Toloo, M., & Nalchigar, S. (2011). A new DEA method for supplier selection in presence of both cardinal and ordinal data. Expert Systems with Applications, 38(12), 14726–14731. doi:10.1016/j.eswa.2011.05.008
  • Toloo, M., Nalchigar, S., & Sohrabi, B. (2018). Selecting most efficient information system projects in presence of user subjective opinions: A DEA approach. Central European Journal of Operations Research, 26(4), 1027–1051. doi:10.1007/s10100-018-0549-4
  • Wang, Y. M., Greatbanks, R., & Yang, J. B. (2005). Interval efficiency assessment using data envelopment analysis. Fuzzy Sets and Systems, 153(3), 347–370. doi:10.1016/j.fss.2004.12.011
  • Zhu, J. (2003). Imprecise data envelopment analysis (IDEA): A review and improvement with an application. European Journal of Operational Research, 144(3), 513–529. doi:10.1016/S0377-2217(01)00392-7
  • Zhu, J. (2004). Imprecise DEA via standard linear DEA models with a revisit to a Korean mobile telecommunication company. Operations Research, 52(2), 323–329. doi:10.1287/opre.1030.0072

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.