طراحی مدل جامع ارزیابی عملکرد همراه با بهبود قدرت تفکیک پذیری واحدهای تصمیم در تحلیل پوششی داده ها به اتکاء سیستم استنتاج فازی
الموضوعات :نوید شریفی 1 , مقصود امیری 2 , لعیا الفت 3 , امیر یوسفلی 4
1 - دانشجوی دکتری گروه مدیریت صنعتی ، دانشکده مدیریت و حسابداری ،دانشگاه علامه طباطبایی،تهران،ایران
2 - استاد گروه مدیریت صنعتی،دانشکده مدیریت و حسابداری ، دانشگاه علامه طباطبائی،تهران،ایران
3 - استاد گروه مدیریت صنعتی،دانشکده مدیریت و حسابداری ، دانشگاه علامه طباطبائی،تهران،ایران
4 - استادیار گروه مهندسی صنایع،دانشکده فنی ، دانشگاه زنجان، زنجان، ایران
الکلمات المفتاحية: تحلیل پوششی دادهها, کارت امتیازی متوازن, مؤسسات آموزش عالی, سیستم استنتاج فازی ممدانی,
ملخص المقالة :
استفاده از مدل تحلیل پوششی دادهها-برای ارزیابی عملکرد و رتبه بندی سازمانها- در حال گسترش است. یکی از چالشهای مهم این مدل، کاهش قدرت تفکیک پذیری واحدهای تصمیمگیر در مواجهه با تعداد زیاد ورودی و خروجی است؛ بنابراین هدف پژوهش، توسعۀ مدل جامع ارزیابی عملکرد همراه با بهبود قدرت تفکیک پذیری واحدهای تصمیمگیر است. در این راستا از کارت امتیازی متوازن برای شناسایی شاخصهای جامع استفاده شد. برای اولین بار-بهطور همزمان-از دو رویکرد عینی و ذهنی مبتنی بر تحلیل عاملی و سیستم استنتاج فازی برای کاهش شاخصها و بهبود قدرت تفکیک پذیری واحدهای تصمیمگیر استفاده شد. پژوهش از حیث روش، توصیفی-تبیینی و از لحاظ جهت گیری تحقیق، کاربردی-توسعه ای است. جامعۀ آماری-برای شناسایی شاخصهای ارزیابی عملکرد و تدوین قوانین استنتاج فازی-خبرگان مؤسسات آموزش عالی شهرستان سمنان بوده است. همچنین بیست و چهار مؤسسه آموزش عالی شهرستان سمنان برای تست مدل انتخاب شدند. ابزارگردآوری دادهها دو پرسشنامه محقق ساخته است. روایی پرسشنامهها به ترتیب توسط روایی محتوا و سازه مورد تأیید قرارگرفت. همچنین پایایی پرسشنامهها به ترتیب به استناد مقدارآلفای کرونباخ و پایایی ترکیبی بیشتر از 0.7مورد تایید است. دستاورد پژوهش را میتوان طراحی مدل تلفیقی با رویکردهای عینی و ذهنی برای بهبود قدرت تفکیک پذیری واحدهای تصمیمگیر دانست. در این خصوص 26 شاخص شناسایی شد و توسط تحلیل عاملی به 8 سازه تقلیل یافت. همچنین با اتکا به سیستم استنتاج فازی طراحی شده،سازهها نمره دهی شدند. نتایج نشان داد:قدرت تفکیک پذیری واحدهای تصمیمگیر توسط مدل پیشنهادی در مقایسه با دیگر مدلهای مرسوم مبتنی بر رویکردهای عینی و ذهنی بیشتر بوده به طوریکه تعداد واحدهای کارا در مدل پیشنهادی به 10 مورد کاهش یافته است. همچنین منتج از نتایج آزمون کروسکال- والیس و محاسبه انحراف معیارنمرۀ کارایی؛ مدل پیشنهادی به ترتیب با کسب میانگین رتبه 48.29 و کسب پراکندگی 0.221دارای رتبه کارایی کمتر و پراکندگی نمره کارایی بیشتر نسبت به سایر مدلها است که تأییدی بر بهبود قدرت تفکیک پذیری مدل پیشنهادی می باشد.
Abirami, G., & Venkataraman, R. (2021). Performance analysis of the dynamic trust model algorithm using the fuzzy inference system for access control. Computers & Electrical Engineering, 92(1), 107132. doi:10.1016/ j.compeleceng.2021.107132
Adler, N., & Golany, B. (2002). Including principal component weights to improve discrimination in data envelopment analysis. Journal of the Operational Research Society, 53(9), 985-991.
Alipour, Nasri, & Faramarz. (2017). Investigation and analysis of educational performance indicators of the University of Marine Sciences by BSC-TOPSIS method. Journal of Marine Science Education, 4 (2), 45-60 [In Persian].
Amiri, M. Ramezanzadeh, M. Khatami Firoozabadi, M & Sedghiani. (2016). Evaluating the performance of scientific departments of Amin University of Law Enforcement Sciences by the common weights approach in data envelopment analysis and fuzzy principal component analysis. Quarterly Journal of Resource Management, (14), 11-36 [In Persian]
Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management science, 39(10), 1261-1264. doi:10.1287/mnsc.39.10.1261
Azar,A. Zarei Mahmoudabadi,M. (2013). Improve performance measurement and resolution in DEA models by introducing a new model. Journal of Improving Management, (20),99-114 [In Persian].
Bagherikahvarin, M., & De Smet, Y. (2016). A ranking method based on DEA and PROMETHEE II (a rank based on DEA & PR. II). Measurement, 89, 333-342.doi:10.1016/j.measurement.2016.04.026
Bal, H., Örkcü, H. H., & Çelebioğlu, S. (2010). Improving the discrimination power and weights dispersion in the data envelopment analysis. Journal of Computers & Operations Research, 37(1), 99-107. doi:10.1016/j.cor.2009. 03.028
Ballou, B., Casey, R. J., Grenier, J. H., & Heitger, D. L. (2012). Exploring the strategic integration of sustainability initiatives: Opportunities for accounting research. Accounting Horizons, 26(2),265-288. doi:10.2308/ acch-50088
Charnes, A., Cooper, W. W., Huang, Z. M., & Sun, D. B. (1990). Polyhedral cone-ratio DEA models with an illustrative application to large commercial banks. Journal of econometrics, 46(1-2), 73-91.doi:10.1016/0304-4076(90) 90048-X
Charnes, A., Cooper, W., Lewin, A. Y., & Seiford, L. M. (1997). Data envelopment analysis theory, methodology and applications. Journal of the Operational Research society, 48(3), 332-333. doi:10.1057/palgrave.jors. 2600342
Chen, X., Liu, X., Gong, Z., & Xie, J. (2021). Three-stage super-efficiency DEA models based on the cooperative game and its application on the R&D green innovation of the Chinese high-tech industry. Computers & Industrial Engineering, 156(6), 107234. doi:10.1016/j.cie.2021.107234
Chen, Y. (2005). Measuring super-efficiency in DEA in the presence of infeasibility. European Journal of Operational Research, 161(2), 545-551. doi:10.1016/j.ejor.2003.08.060
Chen, Y. W., Larbani, M., & Chang, Y. P. (2009). Multiobjective data envelopment analysis. Journal of the Operational Research Society, 60(11), 1556-1566. doi:10.1057/jors.2009.92
Cook, W. D., & Zhu, J. (2014). DEA Cobb–Douglas frontier and cross-efficiency. Journal of the Operational Research Society, 65(2), 265-268. doi:10.1057/jors.2013.13
Cook, W. D., Roll, Y., & Kazakov, A. (1990). A dea model for measuring the relative eeficiency of highway maintenance patrols. Journal of information systems and operational research, 28(2), 113-124. doi:10.1080/03155986. 1990.11732125
Davoudabadi, R., Mousavi, S. M., & Sharifi, E. (2020). An integrated weighting and ranking model based on entropy, DEA and PCA considering two aggregation approaches for resilient supplier selection problem. Journal of Computational Science, 40, 101074.
Deng, F., Xu, L., Fang, Y., Gong, Q., & Li, Z. (2020). PCA-DEA-Tobit regression assessment with carbon emission constraints of China’s logistics industry. Journal of Cleaner Production, 271(8),122548.doi:10.1016/j. jclepro.2020.122548
Dong, F., Zhang, Y., & Zhang, X. (2020). Applying a data envelopment analysis game cross-efficiency model to examining regional ecological efficiency: Evidence from China. Journal of Cleaner Production, 267(6), 122031. doi:10.1016/j.jclepro.2020.122031
Dotoli, M., Epicoco, N., Falagario, M., & Sciancalepore, F. (2015). A cross-efficiency fuzzy data envelopment analysis technique for performance evaluation of decision making units under uncertainty. Journal of Computers & Industrial Engineering, 79, 103-114.doi:10.1016/j.cie.2014. 10.026
Doyle, J. R., & Green, R. H. (1995). Cross-Evaluation In Dea: Improving Discrimination Among Dmusjournal of Information Systems and Operational Research, 33(3), 205-222. doi:10.1080/03155986.1995. 11732281
Dyson, R. G., & Thanassoulis, E. (1988). Reducing weight flexibility in data envelopment analysis. Journal of the operational research society, 39(6), 563-576.
Fallah jelodar, M. (2019). Ranking of efficient decision-making units in data envelopment analysis. Twelfth International Conference of the Iranian Association for Operations Research. [In Persian].
Foroughi, A. A. (2011). A note on “A new method for ranking discovered rules from data mining by DEA”, and a full ranking approach. Journal of Expert Systems with Applications, 38(10), 12913-12916.doi:10.1016/j.eswa.2008. 10.038
Ghasemi, M. R., Ignatius, J., & Emrouznejad, A. (2014). A bi-objective weighted model for improving the discrimination power in MCDEA. European Journal of Operational Research, 233(3), 640-650. [In Persian].
Ghavami, S. M., Borzooei, Z., & Maleki, J. (2020). An effective approach for assessing risk of failure in urban sewer pipelines using a combination of GIS and AHP-DEA. Process Safety and Environmental Protection, 133, 275-285 [In Persian].
Golany, B., & Roll, Y.(1989). An application procedure for DEA. Omega, 17(3), 237-250.
Gupta, P., Mehlawat, M. K., Aggarwal, U., & Charles, V. (2018). An integrated AHP-DEA multi-objective optimization model for sustainable transportation in mining industry. Journal of Resources Policy, 74(4). doi:10.1016/j.resourpol.2018.04.007
Hosseini Iraqi, S. Bakhshi, E. Kahrizi, F. (2016). Integrated model of data envelopment analysis and TOPSIS to evaluate the performance of bank branches. International Conference on Industrial Engineering and Management, 1-18. [In Persian].
Hosseinzadeh Lotfi, F. Kouchaki Tajani, E. (2017). Cross-efficiency and its application in ranking decision-making units with fuzzy inputs and outputs (study on ten dairy companies). The first international conference on fuzzy systems management, 1-15. [In Persian].
Jahanshahloo, G. R., Memariani, A., Lotfi, F. H., & Rezai, H. Z. (2005). A note on some of DEA models and finding efficiency and complete ranking using common set of weights. Journal of Applied mathematics and computation, 166(2), 265-281.
Jenkins, L., & Anderson, M. (2003). A multivariate statistical approach to reducing the number of variables in data envelopment analysis. European Journal of Operational Research, 147(1), 51-61.
Jie, W. U., Liang, L., & ZHA, Y. C. (2008). Determination of the weights of ultimate cross efficiency based on the solution of nucleolus in cooperative game. Systems Engineering-Theory & Practice, 28(5), 92-97. doi:10.1016/S1874-8651(09)60023-5
Kanji, G. K., Malek, A., & Tambi, B. A. (1999). Total quality management in UK higher education institutions.Total Quality Management,10(1),129-153. doi:10.1080/0954412998126
Khalili, M., Camanho, A. S., Portela, M. C. A. S., & Alirezaee, M. R. (2010). The measurement of relative efficiency using data envelopment analysis with assurance regions that link inputs and outputs. European Journal of Operational Research, 203(3), 761-770 [In Persian].
Kumar, A., Shankar, R., & Debnath, R. M. (2015). Analyzing customer preference and measuring relative efficiency in telecom sector: A hybrid fuzzy AHP/DEA study. Telematics and Informatics, 32(3), 447-462. doi:10.1016/j.tele.2014.10.003
Li, S. Jahanshahloo, G. R., Khodabakhshi, M. (2007). A super-efficiency model for ranking units indata envelopment analysis. Journal of Applied Mathematics and Computation. 638-648.
Li, X. B., & Reeves, G. R. (1999). A multiple criteria approach to data envelopment analysis. European Journal of Operational Research, 115(3), 507-517. doi:10.1016/S0377-2217(98)00130-1
Lotfi, F. H., Jahanshahloo, G. R., & Esmaeili, M. (2007). Classification of decision making units with interval data using SBM model. Jornal of Applied Mathematical Sciences, 1(14), 681-689 [In Persian].
Mecit, E. D., & Alp, I. (2013). A new proposed model of restricted data envelopment analysis by correlation coefficients. Journal of Applied Mathematical Modelling, 37(5), 3407-3425.
Mehr Al-Hassani, Mohammad Hossein, Emami, Haghdoost, Dehnaviyeh, Amanpour, ... & Bazar Afshan. (2017). Evaluating the performance of medical universities in the country with a combined approach of balanced scorecard and hierarchical analysis process (AHP-BSC): 2013. Iranian Journal of Epidemiology, 12, 55-64. [In Persian].
Mehrabian, S., Alirezaee, M. R., & Jahanshahloo, G. R. (1999). A complete efficiency ranking of decision making units in data envelopment analysis. Journal of Computational optimization and applications, 14(2), 261-266 [In Persian].
Mehregan, & Dehghan Nairi. (2009). Coherent approach of BSC-TOPSIS to evaluate the top management schools of universities in Tehran province. Journal of Industrial Management, 1 (2), 153-168. [In Persian].
Nobahar,E. Azar,A. (2015) Presenting a model for evaluating the performance of bank branches using the combined approach of PCA and DEA (Case study: third degree branches of agricultural banks). Journal of Organizational Resource Management Research No. 3, 1-22.
Omrani, H. Qarizadeh Birgh, R. Shafiee Kalibari, & Saeed. (2014). Provide a hybrid model for evaluating the performance and ranking of Iranian insurance companies using the opinion of experts. Journal of Industrial Management, 6 (4), 791-807 [In Persian].
Otay, İ., Oztaysi, B., Onar, S. C., & Kahraman, C. (2017). Multi-expert performance evaluation of healthcare institutions using an integrated intuitionistic fuzzy AHP & DEA methodology. Knowledge-Based Systems, 133, 90-106. doi:10.1016/j.knosys.2017.06.028
Rezai Balf F., Zhiani Rezai H., Jahanshahloo G. R., Hosseinzadeh Lotfi, G. R.(2012) "Ranking efficient DMUs using the Tchebycheff norm", journal of Applied Mathematical Modelling, 36: 46–56[In Persian].
Safari, H., Hossein, Kazemi, Alieh, Mehrpour Layeghi, & Ahmad. (2018). Evaluate the performance of the operational areas of the gas transmission company using the DEA-SWARA-WASPAS combined method. Journsl of Industrial Management Studies, 16 (49), 139-171[In Persian].
Sarrico, C. S., & Dyson, R. G.(2004). Restricting virtual weights in data envelopment analysis. European Journal of Operational Research, 159(1), 17-34. doi:10.1016/S0377-2217(03)00402-8
Sexton, T. R.(1986). The methodology of data envelopment analysis. New directions for program evaluation, 32, 7-29.
Shaghli, A., & Roshanas, Kh. (2016). Application of Balanced Scorecard (BSC) and Analytic Hierarchy Process (AHP) in Evaluating the Performance of Scientific Departments: A Case Study in the Faculty of Pharmacy, Zanjan University of Medical Sciences. Journal of Education Development, 9 (22), 53-63[In Persian].
Sim, K. L., & Koh, H. C. (2001). Balanced scorecard: a rising trend in strategic performance measurement. Measuring business excellence, 5(2), 18-27.
Soleimani-Damaneh, M., Jahanshahloo, G. R., & Foroughi, A. A. (2006). A comment on “Measuring super-efficiency in DEA in the presence of infeasibility”. European Journal of Operational Research, 170(1), 323-325. doi:10.1016/j.ejor.2004.09.045
Sueyoshi, T., & Goto, M. (2012). Environmental assessment by DEA radial measurement: US coal-fired power plants in ISO (Independent System Operator) and RTO (Regional Transmission Organization). Energy Economics, 34(3), 663-676. doi:10.1016/j.eneco.2011.08.016
Thompson, R. G., Langemeier, L. N., Lee, C. T., Lee, E., & Thrall, R. M. (1990). The role of multiplier bounds in efficiency analysis with application to Kansas farming. Journal of econometrics, 46(1-2), 93-108. doi:10.1016/0304-4076(90)90049-Y
Thompson, R. G., Singleton Jr, F. D., Thrall, R. M., & Smith, B. A. (1986). Comparative site evaluations for locating a high-energy physics lab in Texas. interfaces, 16(6), 35-49. doi:10.1287/inte.16.6.35
Ünsal, M. G., & Nazman, E. (2020). Investigating socio-economic ranking of cities in Turkey using data envelopment analysis (DEA) and linear discriminant analysis(LDA). Annals of Operations Research,294(1),281-295.
Val Mohammadi, Ch. Firoozeh, N. (2010). Evaluate the performance of the organization using the BSC technique (case study). Journal of Researcher, 7(18), 72-87. [In Persian].
Wang, G., & SU, G. (2013). An Empirical Study of the Economic Sustainable Development Ability of Shandong Province: Based on PCA, DEA and AHP Stratifying Method. Journal of China University of Petroleum (Edition of Social Sciences).
Wang, Y. M., & Chin, K. S. (2011). The use of OWA operator weights for cross-efficiency aggregation. Journal of Omega, 39(5), 493-503. doi:10.1016/j.omega. 2010.10.007
Wang, Z., Hao, H., Gao, F., Zhang, Q., Zhang, J., & Zhou, Y. (2019). Multi-attribute decision making on reverse logistics based on DEA-TOPSIS: A study of the Shanghai End-of-life vehicles industry. Journal of cleaner production, 214, 730-737.
Wong, W. P. (2021). A Global Search Method for Inputs and Outputs in Data Envelopment Analysis: Procedures and Managerial Perspectives. Symmetry, 13(7), 1155.
Wu, T. H., Chung, Y. F., & Huang, S. W. (2021). Evaluating global energy security performances using an integrated PCA/DEA-AR technique. Journal of Sustainable Energy Technologies and Assessments, 45, 101041. doi:10.1016/j.seta.2021.101041
Yang, G.L., Yang,J.B., Liu, W.B., & Li, X.X.(2013). Cross-efficiency aggregation in DEA models using the evidential-reasoning approach. European Journal of Operational Research, 231(2), 393-404. doi:10.1016/j.ejor.2013.05.017
Yarmohammadian, S. Fooladvand, Sh & Badri. (2015). Provide a model for evaluating the performance of universities; Case study of Islamic Azad University, Khorasgan Branch. Journal of New Approach in Educational Management, 6 (22), 19-38.
Yazdan Panah, A & Ehsani, A.(2009). Model of Performance Evaluation Indicators in Higher Education Centers in the Strategic Planning Process Case: Shahid Beheshti University. Journal of Human Resources Research, Imam Hossein University, (1),5. [In Persian].
Zahedi-Seresht, M., Khosravi, S., Jablonsky, J., & Zykova, P. (2021). A data envelopment analysis model for performance evaluation and ranking of DMUs with alternative scenarios. Computers & Industrial Engineering, 1-26. doi:10.1016/j.cie.2020.107002
Zhu, J. (1998). Data envelopment analysis vs. principal component analysis: An illustrative study of economic performance of Chinese cities. European journal of operational research, 111(1), 50-61.
Zimmermann, H. J. (2011). Fuzzy set theory-and its applications. Springer Science & Business Media.
_||_
Abirami, G., & Venkataraman, R. (2021). Performance analysis of the dynamic trust model algorithm using the fuzzy inference system for access control. Computers & Electrical Engineering, 92(1), 107132. doi:10.1016/ j.compeleceng.2021.107132
Adler, N., & Golany, B. (2002). Including principal component weights to improve discrimination in data envelopment analysis. Journal of the Operational Research Society, 53(9), 985-991.
Alipour, Nasri, & Faramarz. (2017). Investigation and analysis of educational performance indicators of the University of Marine Sciences by BSC-TOPSIS method. Journal of Marine Science Education, 4 (2), 45-60 [In Persian].
Amiri, M. Ramezanzadeh, M. Khatami Firoozabadi, M & Sedghiani. (2016). Evaluating the performance of scientific departments of Amin University of Law Enforcement Sciences by the common weights approach in data envelopment analysis and fuzzy principal component analysis. Quarterly Journal of Resource Management, (14), 11-36 [In Persian]
Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management science, 39(10), 1261-1264. doi:10.1287/mnsc.39.10.1261
Azar,A. Zarei Mahmoudabadi,M. (2013). Improve performance measurement and resolution in DEA models by introducing a new model. Journal of Improving Management, (20),99-114 [In Persian].
Bagherikahvarin, M., & De Smet, Y. (2016). A ranking method based on DEA and PROMETHEE II (a rank based on DEA & PR. II). Measurement, 89, 333-342.doi:10.1016/j.measurement.2016.04.026
Bal, H., Örkcü, H. H., & Çelebioğlu, S. (2010). Improving the discrimination power and weights dispersion in the data envelopment analysis. Journal of Computers & Operations Research, 37(1), 99-107. doi:10.1016/j.cor.2009. 03.028
Ballou, B., Casey, R. J., Grenier, J. H., & Heitger, D. L. (2012). Exploring the strategic integration of sustainability initiatives: Opportunities for accounting research. Accounting Horizons, 26(2),265-288. doi:10.2308/ acch-50088
Charnes, A., Cooper, W. W., Huang, Z. M., & Sun, D. B. (1990). Polyhedral cone-ratio DEA models with an illustrative application to large commercial banks. Journal of econometrics, 46(1-2), 73-91.doi:10.1016/0304-4076(90) 90048-X
Charnes, A., Cooper, W., Lewin, A. Y., & Seiford, L. M. (1997). Data envelopment analysis theory, methodology and applications. Journal of the Operational Research society, 48(3), 332-333. doi:10.1057/palgrave.jors. 2600342
Chen, X., Liu, X., Gong, Z., & Xie, J. (2021). Three-stage super-efficiency DEA models based on the cooperative game and its application on the R&D green innovation of the Chinese high-tech industry. Computers & Industrial Engineering, 156(6), 107234. doi:10.1016/j.cie.2021.107234
Chen, Y. (2005). Measuring super-efficiency in DEA in the presence of infeasibility. European Journal of Operational Research, 161(2), 545-551. doi:10.1016/j.ejor.2003.08.060
Chen, Y. W., Larbani, M., & Chang, Y. P. (2009). Multiobjective data envelopment analysis. Journal of the Operational Research Society, 60(11), 1556-1566. doi:10.1057/jors.2009.92
Cook, W. D., & Zhu, J. (2014). DEA Cobb–Douglas frontier and cross-efficiency. Journal of the Operational Research Society, 65(2), 265-268. doi:10.1057/jors.2013.13
Cook, W. D., Roll, Y., & Kazakov, A. (1990). A dea model for measuring the relative eeficiency of highway maintenance patrols. Journal of information systems and operational research, 28(2), 113-124. doi:10.1080/03155986. 1990.11732125
Davoudabadi, R., Mousavi, S. M., & Sharifi, E. (2020). An integrated weighting and ranking model based on entropy, DEA and PCA considering two aggregation approaches for resilient supplier selection problem. Journal of Computational Science, 40, 101074.
Deng, F., Xu, L., Fang, Y., Gong, Q., & Li, Z. (2020). PCA-DEA-Tobit regression assessment with carbon emission constraints of China’s logistics industry. Journal of Cleaner Production, 271(8),122548.doi:10.1016/j. jclepro.2020.122548
Dong, F., Zhang, Y., & Zhang, X. (2020). Applying a data envelopment analysis game cross-efficiency model to examining regional ecological efficiency: Evidence from China. Journal of Cleaner Production, 267(6), 122031. doi:10.1016/j.jclepro.2020.122031
Dotoli, M., Epicoco, N., Falagario, M., & Sciancalepore, F. (2015). A cross-efficiency fuzzy data envelopment analysis technique for performance evaluation of decision making units under uncertainty. Journal of Computers & Industrial Engineering, 79, 103-114.doi:10.1016/j.cie.2014. 10.026
Doyle, J. R., & Green, R. H. (1995). Cross-Evaluation In Dea: Improving Discrimination Among Dmusjournal of Information Systems and Operational Research, 33(3), 205-222. doi:10.1080/03155986.1995. 11732281
Dyson, R. G., & Thanassoulis, E. (1988). Reducing weight flexibility in data envelopment analysis. Journal of the operational research society, 39(6), 563-576.
Fallah jelodar, M. (2019). Ranking of efficient decision-making units in data envelopment analysis. Twelfth International Conference of the Iranian Association for Operations Research. [In Persian].
Foroughi, A. A. (2011). A note on “A new method for ranking discovered rules from data mining by DEA”, and a full ranking approach. Journal of Expert Systems with Applications, 38(10), 12913-12916.doi:10.1016/j.eswa.2008. 10.038
Ghasemi, M. R., Ignatius, J., & Emrouznejad, A. (2014). A bi-objective weighted model for improving the discrimination power in MCDEA. European Journal of Operational Research, 233(3), 640-650. [In Persian].
Ghavami, S. M., Borzooei, Z., & Maleki, J. (2020). An effective approach for assessing risk of failure in urban sewer pipelines using a combination of GIS and AHP-DEA. Process Safety and Environmental Protection, 133, 275-285 [In Persian].
Golany, B., & Roll, Y.(1989). An application procedure for DEA. Omega, 17(3), 237-250.
Gupta, P., Mehlawat, M. K., Aggarwal, U., & Charles, V. (2018). An integrated AHP-DEA multi-objective optimization model for sustainable transportation in mining industry. Journal of Resources Policy, 74(4). doi:10.1016/j.resourpol.2018.04.007
Hosseini Iraqi, S. Bakhshi, E. Kahrizi, F. (2016). Integrated model of data envelopment analysis and TOPSIS to evaluate the performance of bank branches. International Conference on Industrial Engineering and Management, 1-18. [In Persian].
Hosseinzadeh Lotfi, F. Kouchaki Tajani, E. (2017). Cross-efficiency and its application in ranking decision-making units with fuzzy inputs and outputs (study on ten dairy companies). The first international conference on fuzzy systems management, 1-15. [In Persian].
Jahanshahloo, G. R., Memariani, A., Lotfi, F. H., & Rezai, H. Z. (2005). A note on some of DEA models and finding efficiency and complete ranking using common set of weights. Journal of Applied mathematics and computation, 166(2), 265-281.
Jenkins, L., & Anderson, M. (2003). A multivariate statistical approach to reducing the number of variables in data envelopment analysis. European Journal of Operational Research, 147(1), 51-61.
Jie, W. U., Liang, L., & ZHA, Y. C. (2008). Determination of the weights of ultimate cross efficiency based on the solution of nucleolus in cooperative game. Systems Engineering-Theory & Practice, 28(5), 92-97. doi:10.1016/S1874-8651(09)60023-5
Kanji, G. K., Malek, A., & Tambi, B. A. (1999). Total quality management in UK higher education institutions.Total Quality Management,10(1),129-153. doi:10.1080/0954412998126
Khalili, M., Camanho, A. S., Portela, M. C. A. S., & Alirezaee, M. R. (2010). The measurement of relative efficiency using data envelopment analysis with assurance regions that link inputs and outputs. European Journal of Operational Research, 203(3), 761-770 [In Persian].
Kumar, A., Shankar, R., & Debnath, R. M. (2015). Analyzing customer preference and measuring relative efficiency in telecom sector: A hybrid fuzzy AHP/DEA study. Telematics and Informatics, 32(3), 447-462. doi:10.1016/j.tele.2014.10.003
Li, S. Jahanshahloo, G. R., Khodabakhshi, M. (2007). A super-efficiency model for ranking units indata envelopment analysis. Journal of Applied Mathematics and Computation. 638-648.
Li, X. B., & Reeves, G. R. (1999). A multiple criteria approach to data envelopment analysis. European Journal of Operational Research, 115(3), 507-517. doi:10.1016/S0377-2217(98)00130-1
Lotfi, F. H., Jahanshahloo, G. R., & Esmaeili, M. (2007). Classification of decision making units with interval data using SBM model. Jornal of Applied Mathematical Sciences, 1(14), 681-689 [In Persian].
Mecit, E. D., & Alp, I. (2013). A new proposed model of restricted data envelopment analysis by correlation coefficients. Journal of Applied Mathematical Modelling, 37(5), 3407-3425.
Mehr Al-Hassani, Mohammad Hossein, Emami, Haghdoost, Dehnaviyeh, Amanpour, ... & Bazar Afshan. (2017). Evaluating the performance of medical universities in the country with a combined approach of balanced scorecard and hierarchical analysis process (AHP-BSC): 2013. Iranian Journal of Epidemiology, 12, 55-64. [In Persian].
Mehrabian, S., Alirezaee, M. R., & Jahanshahloo, G. R. (1999). A complete efficiency ranking of decision making units in data envelopment analysis. Journal of Computational optimization and applications, 14(2), 261-266 [In Persian].
Mehregan, & Dehghan Nairi. (2009). Coherent approach of BSC-TOPSIS to evaluate the top management schools of universities in Tehran province. Journal of Industrial Management, 1 (2), 153-168. [In Persian].
Nobahar,E. Azar,A. (2015) Presenting a model for evaluating the performance of bank branches using the combined approach of PCA and DEA (Case study: third degree branches of agricultural banks). Journal of Organizational Resource Management Research No. 3, 1-22.
Omrani, H. Qarizadeh Birgh, R. Shafiee Kalibari, & Saeed. (2014). Provide a hybrid model for evaluating the performance and ranking of Iranian insurance companies using the opinion of experts. Journal of Industrial Management, 6 (4), 791-807 [In Persian].
Otay, İ., Oztaysi, B., Onar, S. C., & Kahraman, C. (2017). Multi-expert performance evaluation of healthcare institutions using an integrated intuitionistic fuzzy AHP & DEA methodology. Knowledge-Based Systems, 133, 90-106. doi:10.1016/j.knosys.2017.06.028
Rezai Balf F., Zhiani Rezai H., Jahanshahloo G. R., Hosseinzadeh Lotfi, G. R.(2012) "Ranking efficient DMUs using the Tchebycheff norm", journal of Applied Mathematical Modelling, 36: 46–56[In Persian].
Safari, H., Hossein, Kazemi, Alieh, Mehrpour Layeghi, & Ahmad. (2018). Evaluate the performance of the operational areas of the gas transmission company using the DEA-SWARA-WASPAS combined method. Journsl of Industrial Management Studies, 16 (49), 139-171[In Persian].
Sarrico, C. S., & Dyson, R. G.(2004). Restricting virtual weights in data envelopment analysis. European Journal of Operational Research, 159(1), 17-34. doi:10.1016/S0377-2217(03)00402-8
Sexton, T. R.(1986). The methodology of data envelopment analysis. New directions for program evaluation, 32, 7-29.
Shaghli, A., & Roshanas, Kh. (2016). Application of Balanced Scorecard (BSC) and Analytic Hierarchy Process (AHP) in Evaluating the Performance of Scientific Departments: A Case Study in the Faculty of Pharmacy, Zanjan University of Medical Sciences. Journal of Education Development, 9 (22), 53-63[In Persian].
Sim, K. L., & Koh, H. C. (2001). Balanced scorecard: a rising trend in strategic performance measurement. Measuring business excellence, 5(2), 18-27.
Soleimani-Damaneh, M., Jahanshahloo, G. R., & Foroughi, A. A. (2006). A comment on “Measuring super-efficiency in DEA in the presence of infeasibility”. European Journal of Operational Research, 170(1), 323-325. doi:10.1016/j.ejor.2004.09.045
Sueyoshi, T., & Goto, M. (2012). Environmental assessment by DEA radial measurement: US coal-fired power plants in ISO (Independent System Operator) and RTO (Regional Transmission Organization). Energy Economics, 34(3), 663-676. doi:10.1016/j.eneco.2011.08.016
Thompson, R. G., Langemeier, L. N., Lee, C. T., Lee, E., & Thrall, R. M. (1990). The role of multiplier bounds in efficiency analysis with application to Kansas farming. Journal of econometrics, 46(1-2), 93-108. doi:10.1016/0304-4076(90)90049-Y
Thompson, R. G., Singleton Jr, F. D., Thrall, R. M., & Smith, B. A. (1986). Comparative site evaluations for locating a high-energy physics lab in Texas. interfaces, 16(6), 35-49. doi:10.1287/inte.16.6.35
Ünsal, M. G., & Nazman, E. (2020). Investigating socio-economic ranking of cities in Turkey using data envelopment analysis (DEA) and linear discriminant analysis(LDA). Annals of Operations Research,294(1),281-295.
Val Mohammadi, Ch. Firoozeh, N. (2010). Evaluate the performance of the organization using the BSC technique (case study). Journal of Researcher, 7(18), 72-87. [In Persian].
Wang, G., & SU, G. (2013). An Empirical Study of the Economic Sustainable Development Ability of Shandong Province: Based on PCA, DEA and AHP Stratifying Method. Journal of China University of Petroleum (Edition of Social Sciences).
Wang, Y. M., & Chin, K. S. (2011). The use of OWA operator weights for cross-efficiency aggregation. Journal of Omega, 39(5), 493-503. doi:10.1016/j.omega. 2010.10.007
Wang, Z., Hao, H., Gao, F., Zhang, Q., Zhang, J., & Zhou, Y. (2019). Multi-attribute decision making on reverse logistics based on DEA-TOPSIS: A study of the Shanghai End-of-life vehicles industry. Journal of cleaner production, 214, 730-737.
Wong, W. P. (2021). A Global Search Method for Inputs and Outputs in Data Envelopment Analysis: Procedures and Managerial Perspectives. Symmetry, 13(7), 1155.
Wu, T. H., Chung, Y. F., & Huang, S. W. (2021). Evaluating global energy security performances using an integrated PCA/DEA-AR technique. Journal of Sustainable Energy Technologies and Assessments, 45, 101041. doi:10.1016/j.seta.2021.101041
Yang, G.L., Yang,J.B., Liu, W.B., & Li, X.X.(2013). Cross-efficiency aggregation in DEA models using the evidential-reasoning approach. European Journal of Operational Research, 231(2), 393-404. doi:10.1016/j.ejor.2013.05.017
Yarmohammadian, S. Fooladvand, Sh & Badri. (2015). Provide a model for evaluating the performance of universities; Case study of Islamic Azad University, Khorasgan Branch. Journal of New Approach in Educational Management, 6 (22), 19-38.
Yazdan Panah, A & Ehsani, A.(2009). Model of Performance Evaluation Indicators in Higher Education Centers in the Strategic Planning Process Case: Shahid Beheshti University. Journal of Human Resources Research, Imam Hossein University, (1),5. [In Persian].
Zahedi-Seresht, M., Khosravi, S., Jablonsky, J., & Zykova, P. (2021). A data envelopment analysis model for performance evaluation and ranking of DMUs with alternative scenarios. Computers & Industrial Engineering, 1-26. doi:10.1016/j.cie.2020.107002
Zhu, J. (1998). Data envelopment analysis vs. principal component analysis: An illustrative study of economic performance of Chinese cities. European journal of operational research, 111(1), 50-61.
Zimmermann, H. J. (2011). Fuzzy set theory-and its applications. Springer Science & Business Media.