Avkiran, N. K., & McCrystal, A. (2014). Dynamic network range-adjusted measure vs. dynamic network slacks-based measure. Journal of the operations research society of japan, 57(1), 1-14.https://doi.org/10.15807/jorsj.57.1
Azadi, M. & Jafarian, M. (2015). A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context. Computers & operations research, 54, 274 -285. https://doi.org/10.1016/j.cor.2014.03.002
Babazadeh, R., Razmi, J., & Rabbani, M. (2017). An integrated data envelopment analysisemathematical programming approach to strategic biodiesel supply chain network design problem. Journal of cleaner production, 147, 694-707. https://doi.org/10.1016/j.jclepro.2015.09.038
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30, 1078-1092. https://doi.org/10.1287/mnsc.30.9.1078
Boudaghi, E., Farzipoor Saen, R. (2018). Developing a novel model of data envelopment analysis–discriminant analysis for predicting group membership of suppliers in sustainable supply chain. Computers and operations research, 89, 348–359. https://doi.org/10.1016/j.cor.2017.01.006
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2, 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Chen, CH. M. (2009). A network-DEA model with new efficiency measures to incorporate the dynamic effect in production networks. European journal of operational research, 194(3), 687-699. https://doi.org/10.1016/j.ejor.2007.12.025
Chen, C. M., & Van Dalen, J. (2010). Measuring dynamic efficiency: Theories and an integrated methodology. European journal of operational research, 203(3), 749-760. https://doi.org/10.1016/j.ejor.2009.09.001
Cook, W. D., Zhu, J., Gongbing, B., & Yang, F. (2010). Network DEA: Additive efficiency decomposition. European journal of operational research, 207(2), 1122-1129. https://doi.org/10.1016/j.ejor.2010.05.006
De Camargo Fiorini, P., & Charbel José, j. (2017). Information systems and sustainable supply chain management towards a more sustainable society. International journal of information management, 37(4), 241-249. https://doi.org/10.1016/j.ijinfomgt.2016.12.004
Emrouznejad, A., & Thanassoulis, E. (2005). A mathematical model for dynamic efficiency using data envelopment analysis. Journal of applied mathematics and computation, 160, 363-378.https://doi.org/10.1016/j.amc.2003.09.026
Mariz, F. B., Almeida, M. R., & Aloise, D. (2018). A review of dynamic data envelopment analysis: state of the art and applications. International transactions in operational research, 25(2), 469-505. https://doi.org/10.1111/itor.12468
Fukuyama, H., & Weber, W. L. (2015). Measuring Japanese bank performance: A dynamic network DEA approach. Journal of productivity analysis, 44(3), 249-264. https://doi.org/10.1007/s11123-014-0403-1
Goleij, M. J. (2017). New model of two-stage fuzzy data envelopment analysis with variable scale returns. Decision making and operations research, 2(2), 130-146. (In Persion). https://doi.org/10.22105/DMOR.2018.54145
Genovese, A., Acquaye, A. A., Figueroa, A., & Koh, S. L. (2017). Sustainable supply chain management and the transition towards a circular economy: evidence and some applications. Omega, 66, 344-357. https://doi.org/10.1016/j.omega.2015.05.015
Hatami-Marbini, A., Ebrahimnejad, A., & Lozano, S. (2017). Fuzzy efficiency measures in data envelopment analysis using lexicographic multiobjective approach. Journal of computers & industrial engineering, 105, 362–376. https://doi.org/10.1016/j.cie.2017.01.009
Hosseinzadeh-Lotfi, F., Jahanshahloo, G. R., & Mohammadpour, M. (2013). An extension of cross redundancy of interval scale outputs and inputs in DEA. Hindawi publishing corporation journal of applied mathematics, 7, 635-658. https://doi.org/10.1155/2013/658635
Hosseinzadeh-Lotfi, F., Taeb, Z., & Abbasbandy, S. (2017). Determine the efficiency of time depended units by using data envelopment analysis. International journal of research in industrial engineering, 6(3), 193–201. https://doi.org/10.22105/RIEJ.2017.49156
Hsu, SH., & Kuo, T. (2013). Using DEMATEL to develop a carbon management model of supplier selection in green supply chain management. Journal of cleaner production. 56, 164-172. https://doi.org/10.1016/j.jclepro.2011.09.012
Kao, CH. (2013). Dynamic data envelopment analysis: A relational analysis. European journal of operational research, 227, 325–330. https://doi.org/10.1016/j.ejor.2012.12.012
Khalili-Damghani, K., &
Ghasemi, P. (2016). Uncertain centralized/decentralized production-distribution planning problem in multi-product supply chains: fuzzy mathematical optimization approaches.
Industrial engineering and management systems,15(2)
, 156-172. https://doi.org/10.7232/iems.2016.15.2.156
Madahi, R., & Yazdani, H. (2020). Ranking of decision-making units using data envelopment analysis considering multiple time periods. Decision making and operations research, 5(1), 72-82. (In Persion). https://doi.org/10.22105/DMOR.2020.226087.1143
Moreno, P. & Lozano, S. (2016). Super SBI dynamic network DEA approach to measuring efficiency in the provision of public services. International transactions in operational research, 25(2), 715-735. https://doi.org/10.1111/itor.12257
Nemoto, J., & Goto, M. (1999). Dynamic data envelopment analysis: modeling intertemporal behavior of a firm in the presence of productive inefficiencies. Journal of economics letters, 64,51-56. https://doi.org/10.1016/S0165-1765(99)00070-1
Olfat, L., Amiri, M., Bamdad Soufi, J., & Pishdar, M. (2016). A dynamic network efficiency measurement of airports performance considering sustainable development concept: a fuzzy dynamic network-DEA approach. Journal of air transport management, 57, 272-290. https://doi.org/10.1016/j.jairtraman.2016.08.007
Shabanpour, H., Yousefi, S., & Farzipoor Saen, R. (2017). Forecasting efficiency of green suppliers by dynamic data envelopment analysis and artificial neural networks. Journal of cleaner production, 142, 1098-1107. https://doi.org/10.1016/j.jclepro.2016.08.147
Soleymani Damaneh, R. (2019). Evaluation of continuous two-stage structures: a new multi-objective network data envelopment analysis (MO-NDEA) approach. Industrial management journal, 11(3), 487-516. (In Persion). https://doi.org/10.22059/IMJ.2019.280013.1007585
Tavana, M., Shabanpour, H., Yousefi, S., & Saen, R. F. (2017). A hybrid goal programming and dynamic data envelopment analysis framework for sustainable supplier evaluation.
Neural computing and applications,
28(12), 3683-3696.
10.1007/s00521-016-2274-z
Tavassoli, M., Farzipoor Saen, R., & Faramarzi, G. R. (2015). Developing network data envelopment analysis model for supply chain performance measurement in the presence of zero data. Journal of expert systems 32(3),381-391. https://doi.org/10.1111/exsy.12097
Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European journal of operational research, 130, 498–509. https://doi.org/10.1016/S0377-2217(99)00407-5
Tone, k., & Tsutsui, M. (2010). Dynamic DEA: a slacks-based measure approach. Journal of omega, 38, 3-4. https://doi.org/10.1016/j.omega.2009.07.003
Zha, Y., Liang, N., Wu, M., & Bian, Y. (2016). Efficiency evaluation of banks in China: a dynamic two-stage slacks-based measure approach. Omega, 60, 60-72. https://doi.org/10.1016/j.omega.2014.12.008