1Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran.
2Islamic Azad University-Science & Research Branch, Tehran, Iran.
3Department of Mathematics, Islamic Azad University-Karaj P.O.Box 31485-313, Karaj, Iran.
A characteristic of data envelopment analysis (DEA) is to allow individual decision making units (DMUs) to select the most advantageous weights in calculating their efficiency scores. This flexibility, on the other hand, deters the comparison among DMUs on a common base. For dealing with this difficulty and assessing all the DMUs on the same scale, this paper proposes using a multiple objective linear programming (MOLP) approach for generating a common set of weights in the DEA framework.
 Bouyssou D. (1999), Using DEA as a tool for MCDM: some remarks; Journal of the Operational Research Society 50(9); 974-978.  Charnes A., Cooper W.W. (1961), Management Models and Industrial Applications of Linear Programming; John Wiley, New York.  Charnes A., Cooper W.W., Rhodes E. (1978), Measuring the efficiency of decision making units; European Journal of Operational Research 2; 429-444.  Doyle J.R., Green R.H. (1994), Efficiency and cross-efficiency in DEA: derivatives, meanings and uses; Journal of the Operational Research Society 45; 567-578.  Estellita Lins M.P., Angulo Meza L., Moreira da Silva A.C. (2004), A multi-objective approach to determine alternative targets in data envelopment analysis; Journal of the Operational Research Society 55; 1090-1101.  Giokas D. (1997), The use of goal programming and data envelopment analysis for estimating efficient marginal costs of outputs; Journal of the Operational Research Society 48(3); 319-323.
 Golany B. (1988), An interactive MOLP procedure for the extension of DEA to effectiveness analysis; Journal of the Operational Research Society 39(8); 725-734.  Golany B., Yu G. (1995), A goal programming-discriminant function approach to the estimation of an empirical production function based on DEA results; Journal of Productivity Analysis 6; 171-186.  Jahanshahloo G.R., Memariani A., Lotfi F.H., Rezai H.Z. (2005), A note on some of DEA models and finding efficiency and complete ranking using common set of weights; Applied Mathematics and Computation 166; 265-281.  Joro T., Korhonen P., Wallenius J. (1998), Structural comparison of data envelopment analysis and multiple objective linear programming; Management Science 44; 962-970.  Kao C., Hung H.T. (2005), Data envelopment analysis with common weights: the compromise solution approach; Journal of the Operational Research Society 56; 1196-1203.  Karsak E.E., Ahiska S.S. (2005), Practical common weight multi-criteria decision-making approach with an improved discriminating power for technology selection; International Journal of Production Research 43(8); 1537-1554.  Kornbluth J. (1991), Analysing policy effectiveness using cone restricted data envelopment analysis; Journal of the Operational Research Society 42; 1097-1104.  Roll Y., Cook W.D., Golany B. (1991), Controlling factor weights in data envelopment analysis; IIE Transactions 23(1); 2-9.  Roll Y., Golany B. (1993), Alternate methods of treating factor weights in DEA; Omega 21(1); 99- 109.  Stewart T.J. (1996), Relationships between data envelopment analysis and multicriteria decisionanalysis; Journal of the Operational Research Society 47(5); 654-665.  Xiao Bai L., Reeves G.R. (1999), A multiple criteria approach to data envelopment analysis; European Journal of Operational Research 115; 507-517