How to cite this paper
Wichapa, N., Lawong, A & Donmuen, M. (2021). Ranking DMUs using a novel combination method for integrating the results of relative closeness benevolent and relative closeness aggressive models.International Journal of Data and Network Science, 5(3), 401-416.
Refrences
Al‐Faraj, Taqi N., Alidi, Abdulaziz S., & Bu‐Bshait, Khalid A. (1993). Evaluation of Bank Branches by Means of Data Envelopment Analysis. International Journal of Operations & Production Management, 13(9), 45-52. doi: 10.1108/01443579310043628
Alfares, Hesham K., & Duffuaa, Salih O. (2016). Simulation-Based Evaluation of Criteria Rank-Weighting Methods in Multi-Criteria Decision-Making. International Journal of Information Technology & Decision Making, 15(01), 43-61. doi: 10.1142/s0219622015500315
Amalnick, M, & Saffar, M. (2017). An integrated approach for supply chain assessment from resilience engineering and ergonomics perspectives. Uncertain Supply Chain Management, 5(3), 159-168.
Andersen, P.r, & 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
Anitha, J., & Das, R. (2019). Optimization of process parameters in electro discharge machine using standard deviation and TOPSIS method (Vol. 2177).
Charnes, A. W., Cooper, W. W., & Rhodes, E. (1979). Measuring The Efficiency of Decision Making Units. European Journal of Operational Research, 2, 429-444. doi: 10.1016/0377-2217(78)90138-8
Doyle, J., & Green, R. (1994). Efficiency and cross-efficiency in DEA: Derivations, meanings and uses. Journal of the operational research society, 45(5), 567-578.
Prasad, K., Subbaiah, K., & Prasad, M. (2017). Supplier evaluation and selection through DEA-AHP-GRA integrated ap-proach-A case study. Uncertain Supply Chain Management, 5(4), 369-382. doi: 10.5267/j.uscm.2017.4.001
Fancello, G., Carta, M., & Serra, P. (2020). Data Envelopment Analysis for the assessment of road safety in urban road networks: A comparative study using CCR and BCC models. Case studies on transport policy, 8(3), 736-744. doi: https://doi.org/10.1016/j.cstp.2020.07.007
Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253-290. doi: 10.2307/2343100
He, Ting, Ho, William, Lee Ka Man, Carman, & Xu, Xiaofei. (2012). A fuzzy AHP based integer linear programming model for the multi-criteria transshipment problem. The International Journal of Logistics Management, 23(1), 159-179.
Hosseinzadeh Lotfi, F., Jahanshahloo, G. R., Khodabakhshi, M., Rostamy-Malkhlifeh, M., Moghaddas, Z., & Vaez-Ghasemi, M. (2013). A Review of Ranking Models in Data Envelopment Analysis. Journal of Applied Mathematics, 2013, 492421. doi: 10.1155/2013/492421
Hou, Q., Wang, M., & Zhou, X. (2018). Improved DEA cross efficiency evaluation method based on ideal and anti-ideal points. Discrete Dynamics in Nature and Society, 2018., 1604298. doi: 10.1155/2018/1604298
Jahanshahloo, G. R., Hosseinzadeh Lotfi, F., Khanmohammadi, M., Kazemimanesh, M., & Rezaie, V. (2010). Ranking of units by positive ideal DMU with common weights. Expert Systems with Applications, 37(12), 7483-7488. doi: https://doi.org/10.1016/j.eswa.2010.04.011
Kao, C. (2010). Weight determination for consistently ranking alternatives in multiple criteria decision analysis. Applied Mathematical Modelling - APPL MATH MODEL, 34, 1779-1787. doi: 10.1016/j.apm.2009.09.022
Keshavarz-Ghorabaee, M., Govindan, K., Amiri, M., Zavadskas, E. K., & Antuchevičienė, J. (2019). An integrated type-2 fuzzy decision model based on WASPAS and SECA for evaluation of sustainable manufacturing strategies. Journal of Environmental Engineering and Landscape Management, 27(4), 187-200. doi: 10.3846/jeelm.2019.11367
Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of Objective Weights Using a New Method Based on the Removal Effects of Criteria (MEREC). Symmetry, 13(4), 525.
Lesik, I., Bobrovska, N., Bilichenko, O., Dranus, L., Lykhach, V., Dranus, V., ... & Nazarenko, I. (2020). Assessment of management efficiency and infrastructure development of Ukraine. Management Science Letters, 10(13), 3071-3080.. doi: 10.5267/j.msl.2020.5.016
Liang, L., Yang, F., Cook, W. D., & Zhu, J. (2006). DEA models for supply chain efficiency evaluation. Annals of operations research, 145(1), 35-49..
Liu, S., Hu, Y., Zhang, X., Li, Y., & Liu, L. (2020). Blockchain Service Provider Selection Based on an Integrated BWM-Entropy-TOPSIS Method Under an Intuitionistic Fuzzy Environment. IEEE Access, 8, 104148-104164.. doi: 10.1109/ACCESS.2020.2999367
Lu, T., & Liu, S. T. (2016). Ranking DMUs by comparing DEA cross-efficiency intervals using entropy measures. Entropy, 18(12), 452. doi: 10.3390/e18120452
Naseri, S.H., & Kiaei, H. (2016). Cross-efficiency evaluation by the use of ideal and anti-ideal virtual DMUs’ assessment in DEA. International Journal of Applied Operational Research, 6(3), 69-79.
Nasseri, H., & Kiaei, H. (2019). The New Neutral Secondary Goal based on Ideal DMU Evaluation in Cross-Efficiency. doi: 10.22059/ijms.2019.271179.673434
Sexton, T. R., Silkman, R. H., & Hogan, A. J. (1986). Data envelopment analysis: Critique and extensions. New Directions for Program Evaluation, 1986(32), 73-105. doi: 10.1002/ev.1441
Si, Q., & Ma, Z. (2019). DEA cross-efficiency ranking method based on grey correlation degree and relative entropy. Entropy, 21(10), 966.
Sun, J., Wu, J., & Guo, D. (2013). Performance ranking of units considering ideal and anti-ideal DMU with common weights. Applied Mathematical Modelling, 37(9), 6301-6310. doi: https://doi.org/10.1016/j.apm.2013.01.010
Tofallis, C. (1997a). Input efficiency profiling: An application to airlines. Computers & OR, 24, 253-258. doi: 10.1016/S0305-0548(96)00067-6
Tofallis, C. (1997b). Input efficiency profiling: An application to airlines. Computers & Operations Research, 24(3), 253-258. doi: https://doi.org/10.1016/S0305-0548(96)00067-6
Wang, Y. M., Chin, K. S., & Luo, Y. (2011). Cross-efficiency evaluation based on ideal and anti-ideal decision making units. Expert Systems with Applications, 38(8), 10312-10319. doi: https://doi.org/10.1016/j.eswa.2011.02.116
Wang, Y. M., & Luo, Y. (2006). DEA efficiency assessment using ideal and anti-ideal decision making units. Applied Mathematics and Computation, 173(2), 902-915.doi: https://doi.org/10.1016/j.amc.2005.04.023
Weber, C. A. (1996). A data envelopment analysis approach to measuring vendor performance. Supply Chain Management: An International Journal, 1(1), 28-39. doi: 10.1108/13598549610155242
Wei, C. K., Chen, L. C., Li, R. K., & Tsai, C. H. (2011). Exploration of efficiency underestimation of CCR model: Based on medical sectors with DEA-R model. Expert Systems with Applications, 38(4), 3155-3160. doi: https://doi.org/10.1016/j.eswa.2010.08.108
Wichapa, N., Khokhajaikiat, P., & Chaiphet, K. (2021). Aggregating the results of benevolent and aggressive models by the CRITIC method for ranking of decision-making units: A case study on seven biomass fuel briquettes generated from agricultural waste. Decision Science Letters, 10(1), 79-92.
Wichapa, N., & Khokhajaikiat, P. (2018). A Hybrid Multi-Criteria Analysis Model for Solving the Facility Location-Allocation Problem: A Case Study of Infectious Waste Disposal. Journal of Engineering & Technological Sciences, 50(5).
Wu, J., Sun, J., Zha, Y., & Liang, L. (2011). Ranking approach of cross-efficiency based on improved TOPSIS technique. Journal of Systems Engineering and Electronics, 22(4), 604-608.
Zerafat Angiz, M., Adli, M., & Kamali, M.J. (2013). Cross-ranking of Decision Making Units in Data Envelopment Analysis. Applied Mathematical Modelling, 37(1), 398-405. doi: https://doi.org/10.1016/j.apm.2012.02.038
Alfares, Hesham K., & Duffuaa, Salih O. (2016). Simulation-Based Evaluation of Criteria Rank-Weighting Methods in Multi-Criteria Decision-Making. International Journal of Information Technology & Decision Making, 15(01), 43-61. doi: 10.1142/s0219622015500315
Amalnick, M, & Saffar, M. (2017). An integrated approach for supply chain assessment from resilience engineering and ergonomics perspectives. Uncertain Supply Chain Management, 5(3), 159-168.
Andersen, P.r, & 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
Anitha, J., & Das, R. (2019). Optimization of process parameters in electro discharge machine using standard deviation and TOPSIS method (Vol. 2177).
Charnes, A. W., Cooper, W. W., & Rhodes, E. (1979). Measuring The Efficiency of Decision Making Units. European Journal of Operational Research, 2, 429-444. doi: 10.1016/0377-2217(78)90138-8
Doyle, J., & Green, R. (1994). Efficiency and cross-efficiency in DEA: Derivations, meanings and uses. Journal of the operational research society, 45(5), 567-578.
Prasad, K., Subbaiah, K., & Prasad, M. (2017). Supplier evaluation and selection through DEA-AHP-GRA integrated ap-proach-A case study. Uncertain Supply Chain Management, 5(4), 369-382. doi: 10.5267/j.uscm.2017.4.001
Fancello, G., Carta, M., & Serra, P. (2020). Data Envelopment Analysis for the assessment of road safety in urban road networks: A comparative study using CCR and BCC models. Case studies on transport policy, 8(3), 736-744. doi: https://doi.org/10.1016/j.cstp.2020.07.007
Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253-290. doi: 10.2307/2343100
He, Ting, Ho, William, Lee Ka Man, Carman, & Xu, Xiaofei. (2012). A fuzzy AHP based integer linear programming model for the multi-criteria transshipment problem. The International Journal of Logistics Management, 23(1), 159-179.
Hosseinzadeh Lotfi, F., Jahanshahloo, G. R., Khodabakhshi, M., Rostamy-Malkhlifeh, M., Moghaddas, Z., & Vaez-Ghasemi, M. (2013). A Review of Ranking Models in Data Envelopment Analysis. Journal of Applied Mathematics, 2013, 492421. doi: 10.1155/2013/492421
Hou, Q., Wang, M., & Zhou, X. (2018). Improved DEA cross efficiency evaluation method based on ideal and anti-ideal points. Discrete Dynamics in Nature and Society, 2018., 1604298. doi: 10.1155/2018/1604298
Jahanshahloo, G. R., Hosseinzadeh Lotfi, F., Khanmohammadi, M., Kazemimanesh, M., & Rezaie, V. (2010). Ranking of units by positive ideal DMU with common weights. Expert Systems with Applications, 37(12), 7483-7488. doi: https://doi.org/10.1016/j.eswa.2010.04.011
Kao, C. (2010). Weight determination for consistently ranking alternatives in multiple criteria decision analysis. Applied Mathematical Modelling - APPL MATH MODEL, 34, 1779-1787. doi: 10.1016/j.apm.2009.09.022
Keshavarz-Ghorabaee, M., Govindan, K., Amiri, M., Zavadskas, E. K., & Antuchevičienė, J. (2019). An integrated type-2 fuzzy decision model based on WASPAS and SECA for evaluation of sustainable manufacturing strategies. Journal of Environmental Engineering and Landscape Management, 27(4), 187-200. doi: 10.3846/jeelm.2019.11367
Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of Objective Weights Using a New Method Based on the Removal Effects of Criteria (MEREC). Symmetry, 13(4), 525.
Lesik, I., Bobrovska, N., Bilichenko, O., Dranus, L., Lykhach, V., Dranus, V., ... & Nazarenko, I. (2020). Assessment of management efficiency and infrastructure development of Ukraine. Management Science Letters, 10(13), 3071-3080.. doi: 10.5267/j.msl.2020.5.016
Liang, L., Yang, F., Cook, W. D., & Zhu, J. (2006). DEA models for supply chain efficiency evaluation. Annals of operations research, 145(1), 35-49..
Liu, S., Hu, Y., Zhang, X., Li, Y., & Liu, L. (2020). Blockchain Service Provider Selection Based on an Integrated BWM-Entropy-TOPSIS Method Under an Intuitionistic Fuzzy Environment. IEEE Access, 8, 104148-104164.. doi: 10.1109/ACCESS.2020.2999367
Lu, T., & Liu, S. T. (2016). Ranking DMUs by comparing DEA cross-efficiency intervals using entropy measures. Entropy, 18(12), 452. doi: 10.3390/e18120452
Naseri, S.H., & Kiaei, H. (2016). Cross-efficiency evaluation by the use of ideal and anti-ideal virtual DMUs’ assessment in DEA. International Journal of Applied Operational Research, 6(3), 69-79.
Nasseri, H., & Kiaei, H. (2019). The New Neutral Secondary Goal based on Ideal DMU Evaluation in Cross-Efficiency. doi: 10.22059/ijms.2019.271179.673434
Sexton, T. R., Silkman, R. H., & Hogan, A. J. (1986). Data envelopment analysis: Critique and extensions. New Directions for Program Evaluation, 1986(32), 73-105. doi: 10.1002/ev.1441
Si, Q., & Ma, Z. (2019). DEA cross-efficiency ranking method based on grey correlation degree and relative entropy. Entropy, 21(10), 966.
Sun, J., Wu, J., & Guo, D. (2013). Performance ranking of units considering ideal and anti-ideal DMU with common weights. Applied Mathematical Modelling, 37(9), 6301-6310. doi: https://doi.org/10.1016/j.apm.2013.01.010
Tofallis, C. (1997a). Input efficiency profiling: An application to airlines. Computers & OR, 24, 253-258. doi: 10.1016/S0305-0548(96)00067-6
Tofallis, C. (1997b). Input efficiency profiling: An application to airlines. Computers & Operations Research, 24(3), 253-258. doi: https://doi.org/10.1016/S0305-0548(96)00067-6
Wang, Y. M., Chin, K. S., & Luo, Y. (2011). Cross-efficiency evaluation based on ideal and anti-ideal decision making units. Expert Systems with Applications, 38(8), 10312-10319. doi: https://doi.org/10.1016/j.eswa.2011.02.116
Wang, Y. M., & Luo, Y. (2006). DEA efficiency assessment using ideal and anti-ideal decision making units. Applied Mathematics and Computation, 173(2), 902-915.doi: https://doi.org/10.1016/j.amc.2005.04.023
Weber, C. A. (1996). A data envelopment analysis approach to measuring vendor performance. Supply Chain Management: An International Journal, 1(1), 28-39. doi: 10.1108/13598549610155242
Wei, C. K., Chen, L. C., Li, R. K., & Tsai, C. H. (2011). Exploration of efficiency underestimation of CCR model: Based on medical sectors with DEA-R model. Expert Systems with Applications, 38(4), 3155-3160. doi: https://doi.org/10.1016/j.eswa.2010.08.108
Wichapa, N., Khokhajaikiat, P., & Chaiphet, K. (2021). Aggregating the results of benevolent and aggressive models by the CRITIC method for ranking of decision-making units: A case study on seven biomass fuel briquettes generated from agricultural waste. Decision Science Letters, 10(1), 79-92.
Wichapa, N., & Khokhajaikiat, P. (2018). A Hybrid Multi-Criteria Analysis Model for Solving the Facility Location-Allocation Problem: A Case Study of Infectious Waste Disposal. Journal of Engineering & Technological Sciences, 50(5).
Wu, J., Sun, J., Zha, Y., & Liang, L. (2011). Ranking approach of cross-efficiency based on improved TOPSIS technique. Journal of Systems Engineering and Electronics, 22(4), 604-608.
Zerafat Angiz, M., Adli, M., & Kamali, M.J. (2013). Cross-ranking of Decision Making Units in Data Envelopment Analysis. Applied Mathematical Modelling, 37(1), 398-405. doi: https://doi.org/10.1016/j.apm.2012.02.038