Amirteimoori, A., Azizi, H., & Kordrostami, S. (2020). Double frontier two-stage fuzzy data envelopment analysis. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 28(01), 117-152.
Arteaga, F. J. S., Tavana, M., Di Caprio, D., & Toloo, M. (2019). A dynamic multi-stage slacks-based measure data envelopment analysis model with knowledge accumulation and technological evolution. European Journal of Operational Research, 278(2), 448-462.
Atashpaz-Gargari, E., & Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation (pp. 4661-4667). Singapore: Ieee.
Avkiran, N. K., & Morita, H. (2020). Predicting Japanese bank stock performance with a composite relative efficiency metric: A new investment tool. Pacific-Basin Finance Journal, 18(3), 254-271.
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.
Charnes, A., & Cooper, W. W. (1959). Chance-constrained programming. Management science, 6(1), 73-79.
Cole, R. A., & Gunther, J. W. (1995). Separating the likelihood and timing of bank failure. Journal of Banking & Finance, 19(6), 1073-1089.
Cooper, W. W., Huang, Z., Lelas, V., Li, S. X., & Olesen, O. B. (1998). Chance constrained programming formulations for stochastic characterizations of efficiency and dominance in DEA. Journal of Productivity Analysis, 9(1), 53-79.
Cvilikas, A., & Jurkonyte-Dumbliauskiene, E. (2016). Assessment of risk management economic efficiency applying economic logistic theory. Transformations in Business & Economics, 15(3), 207-219.
Dai, X., Liu, Y., & Qin, R. (2010, June). Modeling fuzzy data envelopment analysis with expectation criterion. In International Conference in Swarm Intelligence (pp. 9-16). Berlin, Heidelberg: Springer.
Foroughi, A. A., & Shureshjani, R. A. (2017). Solving generalized fuzzy data envelopment analysis model: A parametric approach. Central European Journal of Operations Research, 25(4), 889-905.
Gaganis, C., Galariotis, E., Pasiouras, F., & Staikouras, C. (2020). Bank profit efficiency and financial consumer protection policies. Journal of Business Research, 118, 98-116.
Ghosh, I., & Rakshit, D. (2017). Performance evaluation of public sector and private sector banks in india by using CAMEL model–A comparative study. Research Bulletin, 43(2), 110-122.
Goldberg, D. E. (1989). Genetic algorithms in search. Optimization, and Machine Learning (1st. ed.). New York: Addison-Wesley.
Hatami-Marbini, A. (2019). Benchmarking with network DEA in a fuzzy environment. RAIRO-Operations Research, 53(2), 687-703.
Hatami-Marbini, A., Ebrahimnejad, A., & Lozano, S. (2017). Fuzzy efficiency measures in data envelopment analysis using lexicographic multiobjective approach. Computers & Industrial Engineering, 105, 362-376.
Holland, J. H. (1975). Adaptation in natural and artificial systems. University of Michigan Press.
Hu, C. K., Liu, F. B., & Hu, C. F. (2017). Efficiency measures in fuzzy data envelopment analysis with common weights. Journal of Industrial & Management Optimization, 13(1), 237-249.
Jafarian-Moghaddam, A. R., & Ghoseiri, K. (2011). Fuzzy dynamic multi-objective data envelopment analysis model. Expert Systems With Applications, 38(1), 850-855.
Kaymaz, I., & McMahon, C. A. (2005). A response surface method based on weighted regression for structural reliability analysis. Probabilistic Engineering Mechanics, 20(1), 11-17.
Keffala, M. R. (2020). How using derivative instruments and purposes affects performance of Islamic banks? Evidence from CAMELS approach. Global Finance Journal, 100520.
Kenneth, U. O., & Adeniyi, A. M. (2014). Prediction of bank failure using camel and market information: comparative Appraisal of some selected banks in Nigeria. Res J Finance Account, 5(3), 1-17.
Kuah, C. T., Wong, K. Y., & Wong, W. P. (2012). Monte Carlo data envelopment analysis with genetic algorithm for knowledge management performance measurement. Expert Systems with Applications, 39(10), 9348-9358.
Namakin, A., Najafi, S. E., Fallah, M., & Javadi, M. (2018). A New evaluation for solving the fully fuzzy data envelopment analysis with z-numbers. Symmetry, 10(9), 384-397.
Papi, S., Khorramabadi, M., & Lashgarara, S. (2018). Estimating productivity of the provinces of Iran in the health sector using fuzzy data in Imprecise Data Envelopment Analysis (IDEA). Journal of Health Administration, 21(73), 35-48.
Pekkaya, M., & Demir, F. E. (2018). Determining the priorities of CAMELS dimensions based on bank performance. In Dincer H., Hacioglu Ü., & Yüksel S. (Eds.). Global approaches in financial economics, banking, and finance (pp. 445-463): Springer.
Peykani, P., Mohammadi, E., Emrouznejad, A., Pishvaee, M. S., & Rostamy-Malkhalifeh, M. (2019). Fuzzy data envelopment analysis: An adjustable approach. Expert Systems with Applications, 136, 439-452.
Punyangarm, V., Yanpirat, P., Charnsethikul, P., & Lertworasirikul, S. (2006). A Credibility Approach for Fuzzy Stochastic Data Envelopment Analysis (FSDEA). Proceeding of the 7th Asia Pacific Industrial Engineering and Management Systems Conference, Bangkok, Thailand, 1720, 633–644.
Qin, R., & Liu, Y. K. (2010). A new data envelopment analysis model with fuzzy random inputs and outputs. Journal of Applied Mathematics and Computing, 33(1-2), 327-356.
Rostami, M. (2015). Determination of Camels model on bank’s performance. International Journal of Multidisciplinary Research and Development, 2(10), 652-664.
Seçme, N. Y., Bayrakdaroğlu, A., & Kahraman, C. (2009). Fuzzy performance evaluation in Turkish banking sector using analytic hierarchy process and TOPSIS. Expert Systems With Applications, 36(9), 11699-11709.
Sengupta, J. (1982). Decision models in stochastic programming: Operational methods of decision making under uncertainty (vol. 7). North-Holland.
Tajeddini, K. (2011). The effects of innovativeness on effectiveness and efficiency. Education, Business and Society: Contemporary Middle Eastern Issues, 4(1), 6-18.
Toloo, M., & Nalchigar, S. (2009). A new integrated DEA model for finding most BCC-efficient DMU. Applied Mathematical Modelling, 33(1), 597-604.
Vives, X. (2019). Competition and stability in modern banking: A post-crisis perspective. International Journal of Industrial Organization, 64, 55-69.
Wanke, P., Azad, M. A. K., Barros, C. P., & Hassan, M. K. (2016). Predicting efficiency in Islamic banks: An integrated multicriteria decision making (MCDM) approach. Journal of International Financial Markets, Institutions and Money, 45, 126-141.
Wanke, P., Barros, C. P., & Nwaogbe, O. R. (2016). Assessing productive efficiency in Nigerian airports using Fuzzy-DEA. Transport Policy, 49, 9-19.
Yaghoubi, A., & Amiri, M. (2015). Designing a new multi-objective fuzzy stochastic DEA model in a dynamic environment to estimate efficiency of decision making units (Case study: An Iranian petroleum company). Journal of Industrial Engineering and Management Studies, 2(2), 26-42.
Yu, M. M., Lin, C. I., Chen, K. C., & Chen, L. H. (2019). Measuring Taiwanese bank performance: A two-system dynamic network data envelopment analysis approach. Omega, 102145.