Developing a Comprehensive Performance Evaluation Model while Enhancing the Resolution of Decision-Making Units in Data Overlay Analysis through a Fuzzy Inference System
Subject Areas : Management (Operations Research)Navid Sharifi 1 , Maghsood Amiri 2 , Laya Olfat 3 , Amir Yousefli 4
1 - Ph.D. Candidate, Industrial Management Department, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran
2 - Professor, Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'i University,Tehran, Iran
3 - Professor, Department of Industrial Management, School of Management and Accounting, Allameh Tabataba'i University,Tehran, Iran
4 - Assistant Professor, Department of Industrial Engineering, Faculty of Engineering, Zanjan University, Zanjan, Iran
Keywords: Data envelopment analysis, Balanced Scorecard, Mamdani Fuzzy Inference System, higher education institution,
Abstract :
The utilization of data envelopment analysis models for assessing and ranking organizational performance is on the rise. One of the important challenges of this model is the diminishing of the decision-making unit’s precision when dealing with a multitude of inputs and outputs. Hence, the aim of the present research was to develop a comprehensive performance evaluation model while enhancing the resolution of decision-making units. To this end, a balanced scorecard was used to identify comprehensive indicators. At the same time, for the first time, two objective and subjective approaches based on factor analysis and fuzzy inference system were used simultaneously to reduce indicators and improve the resolution of decision-making units. This study used an explanatory-descriptive method and was conducted as an applied-developmental research. The statistical population for identifying performance evaluation indicators and developing fuzzy inference rules included the experts of higher education institutions of Semnan city. Moreover, twenty-four higher education institutions of Semnan city were selected for model testing. The researcher made two questionnaires for the data collection. The validity of the questionnaires was confirmed by content and construct validity, respectively. Also, the reliability of the questionnaires was confirmed by Cronbach's alpha value and composite reliability of more than 0.7 respectively. The main accomplishment of the research can be designing a unified model with objective and subjective approaches to improve the resolution of decision units. In this regard, 26 indicators were identified and reduced to 8 structures by factor analysis. Also, the structures were scored by relying on the designed fuzzy inference system. The results demonstrated a significant improvement in the resolution of decision-making units when utilizing the proposed model, in contrast to conventional models which are mostly based on objective and subjective methods. As a result, the number of effective units in the proposed model effectively reduced to 10. Additionally, the results of the Kruskal-Wallis test and the calculation of the standard deviation of the efficiency scores revealed that the proposed model with an average rating of 48.29 and a dispersion of 0.221 has a lower efficiency rating and a greater dispersion as compered to other models. This finding serves as a confirmation of the enhanced resolution achieved by the proposed model
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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
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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].
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