Interval data without sign restrictions in DEA

Applied Mathematical Modelling

Volumn 38, Issue 7-8, 2014, Pages 2028-2036

  Hatami Marbini, Adel ^a   Emrouznejad, Ali ^b   Agrell, Per J ^a  

^a UNIVERSITÉ CATHOLIQUE DE LOUVAIN   (Belgium)

^b ASTON UNIVERSITY   (United Kingdom)

Author keywords

Data envelopment analysis; Interval data; Negative data in DEA; Semi oriented radial measure

Indexed keywords

ENERGY RESOURCES;

COMPREHENSIVE EVALUATION; DECENTRALIZED ENERGY RESOURCES; EXTERNAL TEMPERATURE; INTERVAL DATA; NEGATIVE DATA IN DEA; SEMI-ORIENTED RADIAL MEASURE; TECHNICAL EFFICIENCY; UPPER AND LOWER BOUNDS;

DATA ENVELOPMENT ANALYSIS;

EID: 84895923604     PISSN: 0307904X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apm.2013.10.027     Document Type: Article

References (42)

1. Charnes A., Cooper W.W., Rhodes E.L. Measuring the efficiency of decision making units. Eur. J. Oper. Res. 1978, 2(6):429-444.
2. Scheel H. Undesirable outputs in efficiency valuations. Eur. J. Oper. Res. 2001, 132:400-410.
3. Portela M.C.A.S., Thanassoulis E., Simpson G. A directional distance approach to deal with negative data in DEA: an application to bank branches. J. Oper. Res. Soc. 2004, 55(10):1111-1121.
4. Sharp J.A., Liu W.B., Meng W. A modified slacks-based measure model for data envelopment analysis with natural negative outputs and inputs. J. Oper. Res. Soc. 2007, 58(12):l672-1677.
5. Emrouznejad A., Anouze A.L., Thanassoulis E. A semi-oriented radial measure for measuring the efficiency of decision making units with negative data, using DEA. Eur. J. Oper. Res. 2010, 200(1):297-304.
6. Emrouznejad A., Amin G.R., Thanassoulis E., Anouze A.L. On the boundedness of the SORM DEA models with negative data. Eur. J. Oper. Res. 2010, 206(1):265-268.
7. Sengupta J.K. A fuzzy systems approach in data envelopment analysis. Comput. Math. Appl. 1992, 24(8-9):259-266.
8. Hatami-Marbini A., Emrouznejad A., Tavana M. A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making. Eur. J. Oper. Res. 2011, 214:457-472.
9. Farrell M.J. The measurement of productive efficiency. J. R. Stat. Soc. 1957, 120(3):253-290.
10. Cooper W.W., Seiford L.M., Tone K. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software 2006, Kluwer Academic Publisher, Dordrecht. second ed.
11. Lovell C.A.K. Measuring the macroeconomic performance of the Taiwanese economy. Int. J. Prod. Econ. 1995, 39:165-178.
12. Seiford L.M., Zhu J. Modeling undesirable factors in efficiency evaluation. Eur. J. Oper. Res. 2002, 142:16-20.
13. Emrouznejad A., Barnett R.P., Gabriel T. Evaluation of research in efficiency and productivity: a survey and analysis of the first 30 years of scholarly literature in DEA. Socio Econ. Planning Sci. 2008, 42(3):151-157.
14. Cooper W.W., Park K.S., Yu G. IDEA and AR-IDEA: models for dealing with imprecise data in DEA. Manage. Sci. 1999, 45:597-607.
15. Cooper W.W., Park K.S., Yu G. IDEA (imprecise data envelopment analysis) with CMDs (column maximum decision making units). J. Oper. Res. Soc. 2001, 52:176-181.
16. Kim S.H., Park C.K., Park K.S. An application of data envelopment analysis in telephone offices evaluation with partial data. Comput. Oper. Res. 1999, 26:59-72.
17. Park K.S. Simplification of the transformations and redundancy of assurance regions in IDEA (imprecise DEA). J. Oper. Res. Soc. 2004, 55:1363-1366.
18. Zhu J. Imprecise data envelopment analysis (IDEA): a review and improvement with an application. Eur. J. Oper. Res. 2003, 144:513-529.
19. Zhu J. Efficiency evaluation with strong ordinal input and output measures. Eur. J. Oper. Res. 2003, 146:477-485.
20. Zhu J. Imprecise DEA via standard linear DEA models with a revisit to a Korean mobile telecommunication company. Oper. Res. 2004, 52:323-329.
21. Cooper W.W., Park K.S., Yu G. An illustrative application of IDEA (imprecise data envelopment analysis) to a Korean mobile telecommunication company. Oper. Res. 2001, 49:807-820.
22. Lee Y.K., Park K.S., Kim S.H. Computers and operations research. Comput. Oper. Res. 2002, 29:1661-1676.
23. Amirteimoori A., Kordrostami S. Multi-component efficiency measurement with imprecise data. Appl. Math. Comput. 2005, 162:1265-1277.
24. Despotis D.K., Smirlis Y.G. Data envelopment analysis with imprecise data. Eur. J. Oper. Res. 2002, 140:24-36.
25. Yu J.R. IDEA by multiple objective programming. International Journal of Innovative Computing, Information and Control 2007, 3(1):21-29.
26. Park K.S. Efficiency bounds and efficiency classifications in DEA with imprecise data. J. Oper. Res. Soc. 2007, 58:533-540.
27. Park K.S. Duality, efficiency computations and interpretations in imprecise DEA. Eur. J. Oper. Res. 2010, 200:289-296.
28. Entani T., Maeda Y., Tanaka H. Dual models of interval DEA and its extension to interval data. Eur. J. Oper. Res. 2002, 136:32-45.
29. Wang Y.M., Greatbanks R., Yang J.B. Interval efficiency assessment using data envelopment analysis. Fuzzy Sets Syst. 2005, 153:347-370.
30. Haghighat M.S., Khorram E. The maximum and minimum number of efficient units in DEA with interval data. Appl. Math. Comput. 2005, 163:919-930.
31. Smirlis Y.G., Maragos E.K., Despotis D.K. Data envelopment analysis with missing values: an interval DEA approach. Appl. Math. Comput. 2006, 177(1):1-10.
32. Kao C. Interval efficiency measures in data envelopment analysis with imprecise data. Eur. J. Oper. Res. 2006, 174:1087-1099.
33. Toloo M., Aghayi N., Rostamy-malkhalifeh M. Measuring overall profit efficiency with interval data. Appl. Math. Comput. 2008, 201:640-649.
34. Emrouznejad A., Rostamy-Malkhalifeh M., Hatami-Marbini A., Tavana M., Aghayi N. An overall profit Malmquist productivity index with fuzzy and interval data. Math. Comput. Modell. 2011, 54:2827-2838.
35. Shokouhi A.H., Hatami-Marbini A., Tavana M., Saati S. A robust optimization approach for imprecise data envelopment analysis. Comput. Ind. Eng. 2010, 59:387-397.
36. Mostafaee A., Saljooghi F.H. Cost efficiency measures in data envelopment analysis with data uncertainty. Eur. J. Oper. Res. 2010, 202:595-603.
37. Färe R., Grosskopf S., Lovell C.A.K. Production Frontiers 1994, Cambridge University Press, Cambridge.
38. Berger A.N., Humphery D. Beyond the black box: what explains differences in the efficiencies of financial institutions?. J. Bank Finance 1997, 21:895-947.
39. Fethi M.D., Pasiouras F. Assessing bank efficiency and performance with operational research and artificial intelligence techniques: a survey. Eur. J. Oper. Res. 2010, 204(2):189-198.
40. Asmild M., Pastor J.T. Slack free MEA and RDM with comprehensive efficiency measures. Omega 2010, 38:475-483.
41. Portela M.C.A.S., Thanassoulis E. Malmquist-type indices in the presence of negative data: an application to bank branches. J. Banking Finance 2010, 34:1472-1483.
42. Ferrier G.D., Lovell C.K. Measuring cost efficiency in banking: econometrics and linear programming evidence. J. Econometrics 1990, 46:229-245.