[1] Roser, M. (2023). Ensure access to affordable, reliable, sustainable and modern energy for all. Our world in data. https://ourworldindata.org/sdgs/affordable-clean-energy
[2] Haegel, N. M., Atwater, H., Barnes, T., Breyer, C., Burrell, A., Chiang, Y.-M., … Bett, A. W. (2019). Terawatt-scale photovoltaics: Transform global energy. Science, 364(6443), 836–838. DOI: 10.1126/science.aaw1845
[3] Wang, C., Li, Z., Ni, X., Shi, W., Zhang, J., Bian, J., & Liu, Y. (2023). Residential water and energy consumption prediction at hourly resolution based on a hybrid machine learning approach. Water research, 246, 120733. https://www.sciencedirect.com/science/article/pii/S0043135423011739
[4] Vartiainen, E., Masson, G., Breyer, C., Moser, D., & Román Medina, E. (2020). Impact of weighted average cost of capital, capital expenditure, and other parameters on future utility-scale PV levelised cost of electricity. Progress in photovoltaics: research and applications, 28(6), 439–453. DOI: 10.1002/pip.3189
[5] Fan, Y., & Stevenson, M. (2018). A review of supply chain risk management: definition, theory, and research agenda. International journal of physical distribution and logistics management, 48(3), 205–230. DOI: 10.1108/IJPDLM-01-2017-0043
[6] Duhamel, F., Carbone, V., & Moatti, V. (2016). The impact of internal and external collaboration on the performance of supply chain risk management. International journal of logistics systems and management, 23(4), 534–557. DOI: 10.1504/IJLSM.2016.075212
[7] Purdy, G. (2010). ISO 31000:2009 - Setting a new standard for risk management: Perspective. Risk analysis, 30(6), 881–886. DOI: 10.1111/j.1539-6924.2010.01442.x
[8] Liu, H. C., You, J. X., Lin, Q. L., & Li, H. (2015). Risk assessment in system FMEA combining fuzzy weighted average with fuzzy decision-making trial and evaluation laboratory. International journal of computer integrated manufacturing, 28(7), 701–714. DOI: 10.1080/0951192X.2014.900865
[9] Wang, Y.-M., Chin, K.-S., Poon, G. K. K., & Yang, J.-B. (2009). Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean. Expert systems with applications, 36(2), 1195–1207.
[10] Ghasemi, N., Najafi, E., Hoseinzadeh Lotfi, F., & Movahedi Sobhani, F. (2020). Assessing the performance of organizations with the hierarchical structure using data envelopment analysis: An efficiency analysis of Farhangian University. Measurement: journal of the international measurement confederation, 156, 107609. DOI: 10.1016/j.measurement.2020.107609
[11] Jahangoshai Rezaee, M., Yousefi, S., Eshkevari, M., Valipour, M., & Saberi, M. (2020). Risk analysis of health, safety and environment in chemical industry integrating linguistic FMEA, fuzzy inference system and fuzzy DEA. Stochastic environmental research and risk assessment, 34(1), 201–218. DOI: 10.1007/s00477-019-01754-3
[12] Rasi Nojehdehi, R., Bagherzadeh Valami, H., & Najafi, S. E. (2023). Classifications of linking activities based on their inefficiencies in Network DEA. International journal of research in industrial engineering, 12(2), 165–176.
[13] Rasinojehdehi, R., & Valami, H. B. (2023). a Comprehensive Neutrosophic Model for Evaluating the Efficiency of Airlines Based on Sbm Model of Network Dea. Decision making: applications in management and engineering, 6(2), 880–906. DOI: 10.31181/dma622023729
[14] Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of mathematical psychology, 15(3), 234–281. DOI: 10.1016/0022-2496(77)90033-5
[15] Shermeh, H. E., Najafi, S. E., & Alavidoost, M. H. (2016). A novel fuzzy network SBM model for data envelopment analysis: A case study in Iran regional power companies. Energy, 112, 686–697. DOI: 10.1016/j.energy.2016.06.087
[16] Rasoulzadeh, M., Edalatpanah, S. A., Fallah, M., & Najafi, S. E. (2022). a Multi-Objective Approach Based on Markowitz and Dea Cross-Efficiency Models for the Intuitionistic Fuzzy Portfolio Selection Problem. Decision making: applications in management and engineering, 5(2), 241–259. DOI: 10.31181/dmame0324062022e
[17] Nojehdehi, R. R., Maleki, P., Abianeh, M., & Valami, H. B. (2012). A geometrical approach for fuzzy production possibility set in data envelopment analysis (DEA) with fuzzy input - output levels. African journal of business management, 6(7), 2738–2745.
[18] Aryanezhad, M. B., Najafi, E., & Bakhshi Farkoush, S. (2011). A BSC-DEA approach to measure the relative efficiency of service industry: A case study of banking sector. International journal of industrial engineering computations, 2(2), 273–282. DOI: 10.5267/j.ijiec.2010.03.004
[19] Bagherzadeh Valami, H., & Raeinojehdehi, R. (2016). Ranking units in Data Envelopment Analysis with fuzzy data. Journal of intelligent and fuzzy systems, 30(5), 2505–2516. DOI: 10.3233/IFS-151756
[20] Chin, K. S., Wang, Y. M., Ka Kwai Poon, G., & Yang, J. B. (2009). Failure mode and effects analysis using a group-based evidential reasoning approach. Computers and operations research, 36(6), 1768–1779. DOI: 10.1016/j.cor.2008.05.002
[21] Sankar, N. R., & Prabhu, B. S. (2001). Modified approach for prioritization of failures in a system failure mode and effects analysis. International journal of quality and reliability management, 18(3), 324–335. DOI: 10.1108/02656710110383737
[22] Chang, C.-L., Wei, C.-C., & Lee, Y.-H. (1999). Failure mode and effects analysis using fuzzy method and grey theory. Kybernetes, 28(9), 1072–1080.
[23] Yang, Z., Bonsall, S., & Wang, J. (2008). Fuzzy rule-based Bayesian reasoning approach for prioritization of failures in FMEA. IEEE transactions on reliability, 57(3), 517–528. DOI: 10.1109/TR.2008.928208
[24] Leo, M., Sharma, S., & Maddulety, K. (2019). Machine learning in banking risk management: A literature review. Risks, 7(1), 29. DOI: 10.3390/risks7010029
[25] Chandrinos, S. K., Sakkas, G., & Lagaros, N. D. (2018). AIRMS: A risk management tool using machine learning. Expert systems with applications, 105, 34–48. DOI: 10.1016/j.eswa.2018.03.044
[26] Gondia, A., Siam, A., El-Dakhakhni, W., & Nassar, A. H. (2020). Machine Learning Algorithms for Construction Projects Delay Risk Prediction. Journal of construction engineering and management, 146(1), 4019085. DOI: 10.1061/(asce)co.1943-7862.0001736
[27] Yokota, S., & Kumano, T. (2013). Mega-solar optimal allocation using data envelopment analysis. Electrical engineering in japan (english translation of denki gakkai ronbunshi), 183(4), 24–32. DOI: 10.1002/eej.22322
[28] Soltanali, H., Khojastehpour, M., E Pais, J. E. de A., & Farinha, J. T. (2022). Sustainable Food Production: An Intelligent Fault Diagnosis Framework for Analyzing the Risk of Critical Processes. Sustainability (switzerland), 14(3), 1083. DOI: 10.3390/su14031083
[29] Soltanali, H., Nikkhah, A., & Rohani, A. (2017). Energy audit of Iranian kiwifruit production using intelligent systems. Energy, 139, 646–654. DOI: 10.1016/j.energy.2017.08.010
[30] Kutyłowska, M. (2015). Neural network approach for failure rate prediction. Engineering failure analysis, 47(PA), 41–48. DOI: 10.1016/j.engfailanal.2014.10.007
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