Several researchers have considered similarities between Multi-Criteria Decision Making (MCDM) and Data Envelopment Analysis (DEA), as tools for solving decision making problems. As the preferences of decision- maker (DM) on alternatives are not considered in classical DEA, some researchers have tried to consider it in DEA.

Makui, A & Momeni, M. (2012). Using CSW weight’s in UTASTAR method.Decision Science Letters, 1(1), 39-46.

Amiri, M., Zandieh, M., Vahdani, B., Soltani, R., & Roshanaei, V. (2010). An integrated eigenvector–DEA–TOPSIS methodology for portfolio risk evaluation in the FOREX spot market. Expert Systems with Applications, 37, 509–516.

Azadeh , A., Ghaderi , S.F., & Izadbakhsh, H. (2008). Integration of DEA and AHP with computer simulation for railway system improvement and optimization. Applied Mathematics and Computation, 195, 775–785.

Hosseinzadeh Lotfi, F., & Jahanshahloo, G.R., Soltanifar, M., Ebrahimnejad, A., Mansourzadeh, S.M. (2009). Relationship between MOLP and DEA based on output-orientated CCR dual model. Expert Systems with Applications , 37(6), 4331-4336.

Jahanshahloo, G.R., Memariani , A., Hosseinzadeh Lotfi, F., & 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.

Li, X.B., Reeves, G.R. (1999). A multiple criteria approach to data envelopment analysis. European Journal of Operational Research, 115, 507-517.

Liu, F.F.H., & Peng, H.H. (2008). Ranking of units on the DEA frontier with common weights. Computers & Operations Research, 35, 1624–1637,(2008)

Liu, F.F.H., & Peng, H.H. (2009). A systematic procedure to obtain a preferable and robust ranking of units. Computers & Operations Research, 36, 1012–1025.

Makui, A., Alinezhad , A., Kiani Mavi, R., & Zohrehbandian, M. (2008). A oal programming method for inding ommon weights in DEA with an improved iscriminating power for fficiency. Journal of Industrial and Systems Engineering, 1(4), 293-303.

Mavrotas, G. & Trifillis, P. (2006). Multicriteria decision analysis with minimum information: combining DEA with MAVT. Computers & Industrial Engineering, 33, 2083-2098.

Ramanathan, R. (2006). Data envelopment analysis for weight derivation and aggregation in the analytic hierarchy process. Computers & Operations Research, 33, 1289–130

Sarkis, J. (2000). A comparative analysis of DEA as a discrete alternative multiple criteria decision tool. European Journal of Operational Research, 123(3), 543-557.

Soares de Mello, J.C.C.B., Lins, M.P.E., Soares de Mello, M.H.C., & Gomes, E.G. (2002). Evaluating the performance of calculus classes using operational research tools. European Journal of Engineering Education, 27(2), 209-218.

Sinuany-Stern, Z., & Friedman, L. (1998). DEA and the discriminant analysis of ratio for ranking units. European Journal of Operational Research, 111,470-478.

Tseng , Y.F., & Lee, T.Z. (2009). Comparing appropriate decision support of human resource practices on organizational performance with DEA/AHP model. Expert Systems with Applications, 36, 6548–6558.

Wang, Y.M., Luo, Y., & Liang, L. (2009). Ranking decision making units by imposing a minimum weight restriction in the data envelopment analysis. Journal of Computational and Applied Mathematics, 223 ,469–484.

Wang, Y.M., Chin, K.S., & Poon, G.K.K. (2008). A data envelopment analysis method with assurance region for weight generation in the analytic hierarchy process. Decision Support Systems, 45 , 913–921.

Wang, Y.M., & Luo, Y. (2010). Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making, Mathematical and Computer Modelling, 51(1-2), 1–12.

Appendix

The new UTASTAR model is as the following:

min



subject to