How to cite this paper
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.
Refrences
Abdel-Basset, M., & Mohamed, R. (2020). A novel plithogenic TOPSIS- CRITIC model for sustainable supply chain risk management. Journal of Cleaner Production, 247, 119586. doi: https://doi.org/10.1016/j.jclepro.2019.119586
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
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.
Bellver, A. J., Cervelló Royo, R. E., & García García, F. (2011). Spanish savings banks and their future transformation into private capital banks.determining their value by a multicriteria valuation methodology. European Journal of Economics, Finance and Administrative Sciences, 35, 155-164.
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
Cook, W. D., Roll, Y., & Kazakov, A. (1990). A dea model for measuring the relative eeficiency of highway maintenance patrols. INFOR: Information Systems and Operational Research, 28(2), 113-124.
Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Alternative Dea Models. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software, 87-130..
Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-770. doi: https://doi.org/10.1016/0305-0548(94)00059-H
Dotoli, M., Epicoco, N., Falagario, M., & Sciancalepore, F. (2016). A stochastic cross‐efficiency data envelopment analysis approach for supplier selection under uncertainty. International Transactions in Operational Research, 23(4), 725-748.. doi: 10.1111/itor.12155
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.
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
Hosseinzadeh, F., Eshlaghy, A., & Shafiee, M. (2012). Providers Ranking Using Data Envelopment Analysis Model, Cross Efficiency and Shannon Entropy. Applied Mathematical Sciences, 6(4), 153-161.
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, 20. doi: 10.1155/2013/492421
Keshavarz Ghorabaee, M. K., Amiri, M., Zavadskas, E. K., & Antucheviciene, J. (2018). A new hybrid fuzzy MCDM approach for evaluation of construction equipment with sustainability considerations. Archives of Civil and Mechanical Engineering, 18, 32-49. doi: https://doi.org/10.1016/j.acme.2017.04.011
Kuah, C., Wong, K., & Behrouzi, F. (2010). A review on Data Envelopment Analysis (DEA). Asia International Conference on Modelling & Simulation, 0, 168-173. doi: 10.1109/AMS.2010.45
Kumar, V., & Singh, H. (2020). Parametric optimization of rotary ultrasonic drilling using grey relational analysis. materials today: Proceedings, 22, 2676-2695. doi: https://doi.org/10.1016/j.matpr.2020.03.399
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, 3071-3080. doi: 10.5267/j.msl.2020.5.016
Li, Xiao-Bai, & Reeves, Gary R. (1999). A multiple criteria approach to data envelopment analysis. European Journal of Operational Research, 115(3), 507-517. doi: https://doi.org/10.1016/S0377-2217(98)00130-1
Liang, L., Wu, J., Cook, W. D., & Zhu, J. (2008). Alternative secondary goals in DEA cross-efficiency evaluation. International Journal of Production Economics, 113(2), 1025-1030. doi: https://doi.org/10.1016/j.ijpe.2007.12.006
Liang, L., Wu, J., Cook, W. D., & Zhu, J. (2008). The DEA game cross-efficiency model and its Nash equilibrium. Operations research, 56(5), 1278-1288. doi: 10.1287/opre.1070.0487
Liu, J. S., Lu, L.Y. Y., & Lu, W.-M. (2016). Research fronts in data envelopment analysis. Omega, 58, 33-45. doi: https://doi.org/10.1016/j.omega.2015.04.004
Lovell, C. A. K., & Pastor, J. T. (1999). Radial DEA models without inputs or without outputs. European Journal of Operational Research, 118(1), 46-51. doi: https://doi.org/10.1016/S0377-2217(98)00338-5
Lu, T., & Liu, S.-T. (2016). Ranking DMUs by comparing DEA cross-efficiency intervals using entropy measures. Entropy, 18, 452. doi: 10.3390/e18120452
Mardani, A., Zavadskas, E. K., Streimikiene, D., Jusoh, A., & Khoshnoudi, M. (2017). A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency. Renewable and Sustainable Energy Reviews, 70, 1298-1322. doi: https://doi.org/10.1016/j.rser.2016.12.030
Niu, T., Zhang, L., Zhang, B., Zhang, B., & Yang, B. (2020). Scientific research efficiency evaluation model based on DEA and its application analysis—take Shanghai as an example. In Recent Trends in Decision Science and Management (pp. 55-70). Springer, Singapore.
Omid, A., & Zegordi, S. (2015). Integrated AHP and network DEA for assessing the efficiency of Iranian handmade carpet industry. Decision Science Letters, 4(4), 477-486. doi: 10.5267/j.dsl.2015.6.002
Promdee, K., Chanvidhwatanakit, J., Satitkune, S., Boonmee, C., Kawichai, T., Jarernprasert, S., & Vitidsant, T. (2017). Characterization of carbon materials and differences from activated carbon particle (ACP) and coal briquettes product (CBP) derived from coconut shell via rotary kiln. Renewable and Sustainable Energy Reviews, 75, 1175-1186.
Rakhshan, S.A. (2017). Efficiency ranking of decision making units in data envelopment analysis by using TOPSIS-DEA method. Journal of the Operational Research Society, 68(8), 906-918.
Rostamzadeh, R., Ghorabaee, M. K., Govindan, K., Esmaeili, A., & Nobar, H. B. K. (2018). Evaluation of sustainable supply chain risk management using an integrated fuzzy TOPSIS-CRITIC approach. Journal of Cleaner Production, 175, 651-669. doi: https://doi.org/10.1016/j.jclepro.2017.12.071
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
Shirouyehzad, H., Lotfi, F. H., & Dabestani, R. (2013). Aggregating the results of ranking models in data envelopment analysis by Shannon’s entropy: a case study in hotel industry. International Journal of Modelling in Operations Management, 3(2), 149-163. doi: 10.1504/IJMOM.2013.055970
Si, Qin, & Ma, Zhanxin. (2019). DEA cross-efficiency ranking method based on grey correlation degree and relative entropy. Entropy, 21(10), 966.
Song, L., & Liu, F. (2018). An improvement in DEA cross-efficiency aggregation based on the Shannon entropy. International Transactions in Operational Research, 25(2), 705-714. doi: 10.1111/itor.12361
Sueyoshi, T. (1999). DEA non-parametric ranking test and index measurement: slack-adjusted DEA and an application to Japanese agriculture cooperatives. Omega, 27(3), 315-326. doi: https://doi.org/10.1016/S0305-0483(98)00057-7
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
Tosun, N. (2006). Determination of optimum parameters for multi-performance characteristics in drilling by using grey relational analysis. The International Journal of Advanced Manufacturing Technology, 28(5), 450-455. doi: 10.1007/s00170-004-2386-y
Tuş, A., & Aytaç Adalı, E. (2019). The new combination with CRITIC and WASPAS methods for the time and attendance software selection problem. OPSEARCH, 56(2), 528-538. doi: 10.1007/s12597-019-00371-6
Vujicic, M., Papic, M., & Blagojević, M. (2017). Comparative analysis of objective techniques for criteria weighing in two MCDM methods on example of an air conditioner selection. Tehnika, 72, 422-429. doi: 10.5937/tehnika1703422V
Wang, Y.-M., & Chin, K.-S. (2010a). A neutral DEA model for cross-efficiency evaluation and its extension. Expert Systems with Applications, 37(5), 3666-3675. doi: https://doi.org/10.1016/j.eswa.2009.10.024
Wang, Y.-M., & Chin, K.-S. (2010b). Some alternative models for DEA cross-efficiency evaluation. International Journal of Production Economics, 128(1), 332-338. doi: https://doi.org/10.1016/j.ijpe.2010.07.032
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
Wei, G., Lei, F., Lin, R., Wang, R., Wei, Y., Wu, J., & Wei, C. (2020). Algorithms for probabilistic uncertain linguistic multiple attribute group decision making based on the GRA and CRITIC method: application to location planning of electric vehicle charging stations. Economic Research-Ekonomska Istraživanja, 33(1), 828-846. doi: 10.1080/1331677X.2020.1734851
Wichapa, N., & Khokhajaikiat, P. (2019). A novel holistic approach for solving the multi-criteria transshipment problem for infectious waste management. Decision Science Letters, 8, 441-454.
Wu, Hua-Wen, Zhen, Jin, & Zhang, Jing. (2020). Urban rail transit operation safety evaluation based on an improved CRITIC method and cloud model. Journal of Rail Transport Planning & Management, 100206. doi: https://doi.org/10.1016/j.jrtpm.2020.100206
Wu, J., Chu, J., Sun, J., Zhu, Q., & Liang, Liang. (2016). Extended secondary goal models for weights selection in DEA cross-efficiency evaluation. Computers & Industrial Engineering, 93, 143-151.
Wu, J., Sun, J., Song, M., & Liang, L. (2013). A ranking method for DMUs with interval data based on dea cross-efficiency evaluation and TOPSIS. Journal of Systems Science and Systems Engineering, 22. doi: 10.1007/s11518-013-5216-7
Wu, J., Sun, J., Zha, Yang, & L., Liang, L. (2011). Ranking approach of cross-efficiency based on improved TOPSIS technique. Journal of Systems Engineering and Electronics, 22(4), 604-608.
Yang, Z., & Wei, X. (2019). The measurement and influences of China's urban total factor energy efficiency under environmental pollution: Based on the game cross-efficiency DEA. Journal of Cleaner Production, 209, 439-450. doi: https://doi.org/10.1016/j.jclepro.2018.10.271
Zhao, M, Wang, X, Yu, J., Xue, L., & Yang, S. (2020). A construction schedule robustness measure based on improved prospect theory and the Copula-CRITIC method. Applied Sciences, 10(6), 2013.
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
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.
Bellver, A. J., Cervelló Royo, R. E., & García García, F. (2011). Spanish savings banks and their future transformation into private capital banks.determining their value by a multicriteria valuation methodology. European Journal of Economics, Finance and Administrative Sciences, 35, 155-164.
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
Cook, W. D., Roll, Y., & Kazakov, A. (1990). A dea model for measuring the relative eeficiency of highway maintenance patrols. INFOR: Information Systems and Operational Research, 28(2), 113-124.
Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Alternative Dea Models. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software, 87-130..
Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-770. doi: https://doi.org/10.1016/0305-0548(94)00059-H
Dotoli, M., Epicoco, N., Falagario, M., & Sciancalepore, F. (2016). A stochastic cross‐efficiency data envelopment analysis approach for supplier selection under uncertainty. International Transactions in Operational Research, 23(4), 725-748.. doi: 10.1111/itor.12155
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.
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
Hosseinzadeh, F., Eshlaghy, A., & Shafiee, M. (2012). Providers Ranking Using Data Envelopment Analysis Model, Cross Efficiency and Shannon Entropy. Applied Mathematical Sciences, 6(4), 153-161.
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, 20. doi: 10.1155/2013/492421
Keshavarz Ghorabaee, M. K., Amiri, M., Zavadskas, E. K., & Antucheviciene, J. (2018). A new hybrid fuzzy MCDM approach for evaluation of construction equipment with sustainability considerations. Archives of Civil and Mechanical Engineering, 18, 32-49. doi: https://doi.org/10.1016/j.acme.2017.04.011
Kuah, C., Wong, K., & Behrouzi, F. (2010). A review on Data Envelopment Analysis (DEA). Asia International Conference on Modelling & Simulation, 0, 168-173. doi: 10.1109/AMS.2010.45
Kumar, V., & Singh, H. (2020). Parametric optimization of rotary ultrasonic drilling using grey relational analysis. materials today: Proceedings, 22, 2676-2695. doi: https://doi.org/10.1016/j.matpr.2020.03.399
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, 3071-3080. doi: 10.5267/j.msl.2020.5.016
Li, Xiao-Bai, & Reeves, Gary R. (1999). A multiple criteria approach to data envelopment analysis. European Journal of Operational Research, 115(3), 507-517. doi: https://doi.org/10.1016/S0377-2217(98)00130-1
Liang, L., Wu, J., Cook, W. D., & Zhu, J. (2008). Alternative secondary goals in DEA cross-efficiency evaluation. International Journal of Production Economics, 113(2), 1025-1030. doi: https://doi.org/10.1016/j.ijpe.2007.12.006
Liang, L., Wu, J., Cook, W. D., & Zhu, J. (2008). The DEA game cross-efficiency model and its Nash equilibrium. Operations research, 56(5), 1278-1288. doi: 10.1287/opre.1070.0487
Liu, J. S., Lu, L.Y. Y., & Lu, W.-M. (2016). Research fronts in data envelopment analysis. Omega, 58, 33-45. doi: https://doi.org/10.1016/j.omega.2015.04.004
Lovell, C. A. K., & Pastor, J. T. (1999). Radial DEA models without inputs or without outputs. European Journal of Operational Research, 118(1), 46-51. doi: https://doi.org/10.1016/S0377-2217(98)00338-5
Lu, T., & Liu, S.-T. (2016). Ranking DMUs by comparing DEA cross-efficiency intervals using entropy measures. Entropy, 18, 452. doi: 10.3390/e18120452
Mardani, A., Zavadskas, E. K., Streimikiene, D., Jusoh, A., & Khoshnoudi, M. (2017). A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency. Renewable and Sustainable Energy Reviews, 70, 1298-1322. doi: https://doi.org/10.1016/j.rser.2016.12.030
Niu, T., Zhang, L., Zhang, B., Zhang, B., & Yang, B. (2020). Scientific research efficiency evaluation model based on DEA and its application analysis—take Shanghai as an example. In Recent Trends in Decision Science and Management (pp. 55-70). Springer, Singapore.
Omid, A., & Zegordi, S. (2015). Integrated AHP and network DEA for assessing the efficiency of Iranian handmade carpet industry. Decision Science Letters, 4(4), 477-486. doi: 10.5267/j.dsl.2015.6.002
Promdee, K., Chanvidhwatanakit, J., Satitkune, S., Boonmee, C., Kawichai, T., Jarernprasert, S., & Vitidsant, T. (2017). Characterization of carbon materials and differences from activated carbon particle (ACP) and coal briquettes product (CBP) derived from coconut shell via rotary kiln. Renewable and Sustainable Energy Reviews, 75, 1175-1186.
Rakhshan, S.A. (2017). Efficiency ranking of decision making units in data envelopment analysis by using TOPSIS-DEA method. Journal of the Operational Research Society, 68(8), 906-918.
Rostamzadeh, R., Ghorabaee, M. K., Govindan, K., Esmaeili, A., & Nobar, H. B. K. (2018). Evaluation of sustainable supply chain risk management using an integrated fuzzy TOPSIS-CRITIC approach. Journal of Cleaner Production, 175, 651-669. doi: https://doi.org/10.1016/j.jclepro.2017.12.071
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
Shirouyehzad, H., Lotfi, F. H., & Dabestani, R. (2013). Aggregating the results of ranking models in data envelopment analysis by Shannon’s entropy: a case study in hotel industry. International Journal of Modelling in Operations Management, 3(2), 149-163. doi: 10.1504/IJMOM.2013.055970
Si, Qin, & Ma, Zhanxin. (2019). DEA cross-efficiency ranking method based on grey correlation degree and relative entropy. Entropy, 21(10), 966.
Song, L., & Liu, F. (2018). An improvement in DEA cross-efficiency aggregation based on the Shannon entropy. International Transactions in Operational Research, 25(2), 705-714. doi: 10.1111/itor.12361
Sueyoshi, T. (1999). DEA non-parametric ranking test and index measurement: slack-adjusted DEA and an application to Japanese agriculture cooperatives. Omega, 27(3), 315-326. doi: https://doi.org/10.1016/S0305-0483(98)00057-7
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
Tosun, N. (2006). Determination of optimum parameters for multi-performance characteristics in drilling by using grey relational analysis. The International Journal of Advanced Manufacturing Technology, 28(5), 450-455. doi: 10.1007/s00170-004-2386-y
Tuş, A., & Aytaç Adalı, E. (2019). The new combination with CRITIC and WASPAS methods for the time and attendance software selection problem. OPSEARCH, 56(2), 528-538. doi: 10.1007/s12597-019-00371-6
Vujicic, M., Papic, M., & Blagojević, M. (2017). Comparative analysis of objective techniques for criteria weighing in two MCDM methods on example of an air conditioner selection. Tehnika, 72, 422-429. doi: 10.5937/tehnika1703422V
Wang, Y.-M., & Chin, K.-S. (2010a). A neutral DEA model for cross-efficiency evaluation and its extension. Expert Systems with Applications, 37(5), 3666-3675. doi: https://doi.org/10.1016/j.eswa.2009.10.024
Wang, Y.-M., & Chin, K.-S. (2010b). Some alternative models for DEA cross-efficiency evaluation. International Journal of Production Economics, 128(1), 332-338. doi: https://doi.org/10.1016/j.ijpe.2010.07.032
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
Wei, G., Lei, F., Lin, R., Wang, R., Wei, Y., Wu, J., & Wei, C. (2020). Algorithms for probabilistic uncertain linguistic multiple attribute group decision making based on the GRA and CRITIC method: application to location planning of electric vehicle charging stations. Economic Research-Ekonomska Istraživanja, 33(1), 828-846. doi: 10.1080/1331677X.2020.1734851
Wichapa, N., & Khokhajaikiat, P. (2019). A novel holistic approach for solving the multi-criteria transshipment problem for infectious waste management. Decision Science Letters, 8, 441-454.
Wu, Hua-Wen, Zhen, Jin, & Zhang, Jing. (2020). Urban rail transit operation safety evaluation based on an improved CRITIC method and cloud model. Journal of Rail Transport Planning & Management, 100206. doi: https://doi.org/10.1016/j.jrtpm.2020.100206
Wu, J., Chu, J., Sun, J., Zhu, Q., & Liang, Liang. (2016). Extended secondary goal models for weights selection in DEA cross-efficiency evaluation. Computers & Industrial Engineering, 93, 143-151.
Wu, J., Sun, J., Song, M., & Liang, L. (2013). A ranking method for DMUs with interval data based on dea cross-efficiency evaluation and TOPSIS. Journal of Systems Science and Systems Engineering, 22. doi: 10.1007/s11518-013-5216-7
Wu, J., Sun, J., Zha, Yang, & L., Liang, L. (2011). Ranking approach of cross-efficiency based on improved TOPSIS technique. Journal of Systems Engineering and Electronics, 22(4), 604-608.
Yang, Z., & Wei, X. (2019). The measurement and influences of China's urban total factor energy efficiency under environmental pollution: Based on the game cross-efficiency DEA. Journal of Cleaner Production, 209, 439-450. doi: https://doi.org/10.1016/j.jclepro.2018.10.271
Zhao, M, Wang, X, Yu, J., Xue, L., & Yang, S. (2020). A construction schedule robustness measure based on improved prospect theory and the Copula-CRITIC method. Applied Sciences, 10(6), 2013.