Ahmady, N., Azadi, M., Sadeghi, S. A. H., & Farzipoor Saen, R. (2013). A novel fuzzy data envelopment analysis model with double frontiers for supplier selection. International journal of logistics research and applications, 16(2), 87-98. https://doi.org/10.1080/13675567.2013.772957
Amirteimoori, A. (2007). DEA efficiency analysis: Efficient and anti-efficient frontier. Applied mathematics and computation, 186(1), 10-16. https://doi.org/10.1016/j.amc.2006.07.006
Azizi, H. (2014). DEA efficiency analysis: A DEA approach with double frontiers. International journal of systems science, 45(11), 2289-2300. https://doi.org/10.1080/00207721.2013.768715
Azizi, H., & Ganjeh Ajirlu, H. (2011). Measurement of the worst practice of decision-making units in the presence of non-discretionary factors and imprecise data. Applied mathematical modelling, 35(9), 4149-4156. https://doi.org/10.1016/j.apm.2011.02.038
Azizi, H., Kordrostami, S., & Amirteimoori, A. (2015). Slacks-based measures of efficiency in imprecise data envelopment analysis: An approach based on data envelopment analysis with double frontiers. Computers & industrial engineering, 79, 42-51. https://doi.org/10.1016/j.cie.2014.10.019
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management science, 30(9), 1078-1092. http://dx.doi.org/10.1287/mnsc.30.9.1078
Beasley, J. E. (1990). Comparing university departments. Omega, 18 (2), 171-183. https://doi.org/10.1016/0305-0483(90)90064-G
Beasley, J. E. (1995). Determining Teaching and Research Efficiencies. Journal of the operational research society, 46(4), 441-452. https://doi.org/10.1057/jors.1995.63
Bottani, E., & Rizzi, A. (2006). A fuzzy TOPSIS methodology to support outsourcing of logistics services. Supply chain management: An international journal, 11(4), 294-308. https://doi.org/10.1108/13598540610671743
Cao, J., Wang, W. W., & Cao, G. (2007). Integration of the social welfare function and TOPSIS algorithm for 3PL selection. Proceedings of the fourth international conference on fuzzy systems and knowledge discovery (pp. 596-600). https://doi.org/10.1109/FSKD.2007.367
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444. https://doi.org/10.1016/0377-2217(78)90138-8
Cook, W. D., Green, R. H., & Zhu, J. (2006). Dual-role factors in data envelopment analysis. IIE transactions, 38(2), 105-115. https://doi.org/10.1080/07408170500245570
Cook, W. D., & Zhu, J. (2007). Classifying inputs and outputs in data envelopment analysis. European journal of operational research, 180(2), 692-699. https://doi.org/10.1016/j.ejor.2006.03.048
Efendigil, T., Önüt, S., & Kongar, E. (2008). A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness. Computers & industrial engineering, 54(2), 269-287. https://doi.org/10.1016/j.cie.2007.07.009
Farzipoor Saen, R. (2009). A mathematical model for selecting third-party reverse logistics providers. International journal of procurement management, 2(2), 180-190. https://doi.org/10.1504/IJPM.2009.023406
Farzipoor Saen, R. (2010). Restricting weights in supplier selection decisions in the presence of dual-role factors. Applied mathematical modelling, 34(10), 2820-2830. https://doi.org/10.1016/j.apm.2009.12.016
Haas, D. A., Murphy, F. H., & Lancioni, R. A. (2003). Managing reverse logistics channels with data envelopment analysis. Transportation journal, 42(3), 59-69. https://www.jstor.org/stable/20713532
Liu, F. H. F., & Chen, C. L. (2009). The worst-practice DEA model with slack-based measurement. Computers & industrial engineering, 57(2), 496-505. https://doi.org/10.1016/j.cie.2007.12.021
Meade, L., & Sarkis, J. (2002). A conceptual model for selecting and evaluating third-party reverse logistics providers. Supply chain management: An international journal, 7(5), 283-295. https://doi.org/10.1108/13598540210447728
Min, H., & Joo, S. J. (2006). Benchmarking the operational efficiency of third party logistics providers using data envelopment analysis. Supply chain management: An international journal, 11(3), 259-265. https://doi.org/10.1108/13598540610662167
Qureshi, M. N., Kumar, D., & Kumar, P. (2007). Selection of potential 3PL services providers using TOPSIS with interval data. IEEE international conference on industrial engineering and engineering management (pp. 1512-1516). https://doi.org/10.1109/IEEM.2007.4419445
Roozbeh, F., & Ahadzadeh Namin, M. (2019). Super efficiency model in front of MPSS. International journal of data envelopment analysis, 7(4), 41-46. http://ijdea.srbiau.ac.ir/article_15268.html
Talluri, S., & Baker, R. C. (2002). A multi-phase mathematical programming approach for effective supply chain design. European journal of operational research, 141(3), 544-558. https://doi.org/10.1016/S0377-2217(01)00277-6
Ye, F. F., Yang, L. H., & Wang, Y. M. (2020). An interval efficiency evaluation model for air pollution management based on indicators integration and different perspectives. Journal of cleaner production, 245, 118945. https://doi.org/10.1016/j.jclepro.2019.118945
Zhang, C. Q., & Lu, Y. C. (2006). The reverse logistics evaluation based on Kpca-Linmap model. Proceedings of the fifth international conference on machine learning and cybernetics (pp. 2531-2535). https://doi.org/10.1109/ICMLC.2006.258844
Zhang, H., Li, X., Liu, W., Li, B., &Zhang, Z. (2004). An application of the AHP in 3PL vendor selection of a 4PL system. Proceeding of the IEEE international conference on systems, man and cybernetics (pp. 1255-1260). https://doi.org/10.1109/ICSMC.2004.1399797
Zhang, Y., & Feng, Y. (2007). A Selection approach of reverse logistics provider based on fuzzy AHP. Proceedings of the fourth international conference on fuzzy systems and knowledge discovery (pp. 479–482). https://doi.org/10.1109/FSKD.2007.119
)