Ranking extreme and non-extreme efficient DMUs on the basis of MPSS in DEA
Subject Areas : International Journal of Data Envelopment Analysis
Javad Gerami
1
,
Javad Vakili
2
1 - عضو هیات علمی دانشگاه آزاد اسلامی واحد شیراز
2 - Department of Applied Mathematics, School of Mathematics Science, University of Tabriz, Tabriz, Iran.
Keywords: Data envelopment analysis, Efficiency, Extreme efficient, ranking, productivity.,
Abstract :
Finding units with the most productive scale size (MPSS) is very important. The use of MPSS in ranking is thus the main idea in this paper. We propose an algorithm in DEA that ranks all extreme and non-extreme efficient DMUs in a number of steps. In this method, units with the most productive scale size are identified in each step and are then ranked. We finally show the application of the method using a numerical example.
[1] Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision-making units. European journal of operational research. 1978 Nov 1; 2(6): 429-44.
[2] Banker RD, Charnes A, Cooper WW. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science. 1984 Sep; 30(9): 1078-92.
[3] Charnes A, Cooper WW, Golany B, Seiford L, Stutz J. Foundations of data envelopment analysis for Pareto–Koopmans efficient empirical production functions', Journal of Econometrics. 1985 30: 91–107.
[4] Sexton TR, Silkman RH, Hogan AJ. Data envelopment analysis: critique and extensions. In: Silkman, R.H. (ed.) Measuring efficiency: an assessment of data envelopment analysis. 1986 73-105.
[5] Andersen P, Petersen NC. A procedure for ranking efficient units in data envelopment analysis. Management science. 1993 Oct; 39(10): 1261-4.
[6] Mehrabian S, Alirezaee MR, Jahanshahloo, GR, A complete efficiency ranking of decision making units in data envelopment analysis. Computational Optimization and Applications, 1999 14: 261-266.
[7] Saati MS, Zarafat Angiz M, Jahanshahloo GR. A model for ranking decision making units in data envelopment analysis. Ricerca Operativa. 2001 31(97): 4759.
[8] Sinuany-stern Z, Mehrez A, Hadad Y. An AHP/DEA methodology for ranking decision making units, International Transactions in Operation Research. 20007 109-124.
[9] Jahanshahloo GR, junior HV, Lotfi FH, Akbarian D. A new DEA ranking system based on changing refrence set, European Journal of Operational Research. 2007 181: 331-337.
[10] Jahanshahloo GR, Hosseinzadeh Lotfi F, Shoja N, Tohidi G, Razavian S. Ranking using norm in data envelopment analysis. Applied mathematics and computational. 2004 153 (1): 215-224.
[11] Cooper WW, Li S, Seiford LM, Tone T. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, Reverences and DEA solver Software, Kluwer Academic Publisher, Norwell, Mass. 1999.
[12]Rajiv Banker D. Estimating most productive scale size using data envelopment analysis. European Journal of Operational Research. 1984 17: 35-44.
[13] Juan Du, Liang Liang, Joe Zhu, A slacks-based measure of super-efficiency in data envelopment analysis: A comment. European Journal of Operational Research, 2010 204: 694–697.
[14] Gerami J, Mozaffari MR, Wanke PF. A multi-criteria ratio-based approach for two-stage data envelopment analysis. Expert Systems with Applications. 2020 158: 113508.
[15] Gerami J, Kiani Mavi R, Farzipoor Saen R, Kiani Mavi N. A novel network DEA-R model for evaluating hospital services supply chain performance. Annals of Operations Research. 2020, 324, 1–2: 1041–1066.
[16] Gerami J. An interactive procedure to improve estimate of value efficiency in DEA. Expert Systems with Applications. 15 December 2019, 137 29-45.
[17] Gerami J, Mozaffari MR, Wanke PF, Correa H. A novel slacks-based model for efficiency and super-efficiency in DEA-R. Operations Research. 2021 22, 4: 3373–3410.
[18] Gerami J. Strategic alliances and partnerships based on the semi-additive production technology in DEA. Expert Systems with Applications1 October 2024 251: 123986.