The maximum and minimum number of efficient units in DEA with interval data

Applied Mathematics and Computation

Volumn 163, Issue 2, 2005, Pages 919-930

  Haghighat, Mehdi Sohrabi ^a   Khorram, Esmaile ^a  

^a AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Data envelopment analysis; Discrimination; Interval data

Indexed keywords

ALGORITHMS; LINEAR PROGRAMMING; MATHEMATICAL MODELS; MATHEMATICAL OPERATORS; MATRIX ALGEBRA; PARAMETER ESTIMATION; PROBLEM SOLVING; VECTORS;

DATA ENVELOPMENT ANALYSIS (DEA); DISCRIMINATION; INTERVAL DATA; MODEL FORMULATION;

DATA REDUCTION;

EID: 13544271681     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amc.2004.04.018     Document Type: Article

References (9)

1. Charnes A., Cooper W.W., Rhodes E. Measuring the efficiency of decision making unit. European Journal of Operational Research. 2:1978;429-444
2. Despotis D.K., Smirlis Y.G. Data envelopment analysis with imprecise data. European Journal of Operational Research. 140:2002;24-36
3. Sarkis J., Talluri S. A decision model for evaluation of flexible manufacturing systems in the presence of both cardinal and ordinal factors. International Journal of Production Research. 37:1999;2927-2938
4. Zhu J. Imprecise data envelopment analysis (IDEA): a review and improvement with an application. European Journal of Operational Research. 144:2003;513-529
5. Tone K. Measurement and Improvement of Efficiency by DEA. 1993;Nikkagiren Publishers. (in Japanese)
6. Cook W.D., Doyle J., Green R., Kress M. Multiple criteria modeling and ordinal data: evaluation in terms of subset of criteria. European Journal of Operational Research. 98:1997;602-609
7. Cook W.D., Kress M., Siford L. Data envelopment analysis in the presence of both quantitative and qualitative factors. Journal of the Operational Research Society. 47:1996;945-953
8. Cook W.D., Kress M., Siford L. On the use of ordinal data in data envelopment analysis. Journal of the Operational Research Society. 44:1993;133-140
9. Cooper W.W., Park K.S., Yu G. IDEA and AR-IDEA: models for dealing with imprecise data in DEA. Management Science. 45:1999;597-607