Subject Areas : International Journal of Data Envelopment Analysis
1 -
Keywords:
Abstract :
References:
1) Bachchhav, B., Bharne, S., Choudhari, A., & Pattanshetti, S. (2023). Selection of spot welding electrode material by AHP, TOPSIS, and SAW. Materials Today: Proceedings,2023. https://doi.org/10.1016/j.matpr.2023.02.253.
2) Celen, A. (2013). Efficiency and productivity (TFP) of the Turkish electricity distribution companies: An application of two-stage (DEA & Tobit) analysis. Energy Policy, 63, 300-310.
3) Charnes, A., & Cooper, W.W. (1985). Preface to topics in data envelopment analysis. Annals of Operational Research, 2, 59- 70.
4) Charnes, A., Cooper, W.W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2, 429-444.
5) Chia-Nan Wang, Ch, N., Dang, T.T., Nguyen, A., N., T. & Wang, J.W. (2022). A combined Data Envelopment Analysis (DEA) and Grey Based Multiple Criteria Decision Making (G-MCDM) for solar PV power plants site selection: A case study in Vietnam. Energy Reports, 8, 1124-1142.
6) Chiang, B.C., Tsai, S.L., & Wang, C.C. (2002). Machine vision-based gray relational theory applied to IC marking inspection. IEEE Transactions on Semiconductor Manufacturing, 15 (4), 531-539.
7) Deng, J. (1989). Introduction to grey system theory. The Journal of Grey Systems, 1, 1-24.
8) Deng, J.L. (1982). Control problems of grey system. Systems and Control Letters, 1, 288–294.
9) Deng, J.L. (1992). Grey units. The Journal of Grey Systems, 4(2), 13-29.
10) Ebrahimzadeh Shermeh, H., Darvishnia, R., & Alavidoost, M.H. Evaluating the Efficiency of Power Companies Using Data Envelopment Analysis Based on SBM Models: A Case Study in Power Industry of Iran. Journal of Applied Research on Industrial Engineering, 5(4), 286-295. https://doi.org/10.22105/jarie.2018.81051.
11) Färe, R. (1991). Measuring Farrell efficiency for a firm with intermediate inputs. Academia economic papers, 19(2), 329-340.
12) Färe, R., & Grosskopf, Sh. (2000). Theory and Application of Directional Distance Functions. Journal of Productivity Analysis, 13(2), 93 – 103.
13) Färe, R., Grosskopf, Sh., & Tyteca, D. (1996). An activity analysis model of the environmental performance of firms—application to fossil-fuel-fired electric utilities. Ecological Economics, 18(2), 161-175.https://doi.org/10.1016/0921-8009(96)00019-5.
14) Färe, R., & Whittaker, G. (1995). An Intermediate Input Model of Dairy Production Using Complex Survey Data. Journal of Agricultural Economist, 46(2), 201-213.
15) Fung, C. P. (2003). Manufacturing process optimization for wear property of fiber-reinforced poly butylene terephthalate composites with grey relational analysis. Wear, 254, 298– 306. https://doi.org/10.1016/S0043-1648(03)00013-9
16) Hezer, S., Gelmez, E., & Özceylan, E. (2021). Comparative analysis of TOPSIS, VIKOR and COPRAS methods for the COVID-19 Regional Safety Assessment. Journal of Infection and Public Health, 14(6), 775-786. https://doi.org/10.1016/j.jiph.2021.03.003
17) Hisa, K.H., Chen, M.Y., & Chang, M.C. (2004). Comments on data pre-processing for grey relational analysis. Journal of Grey System, 7(1), 15-20.
18) Hwang, C.L., & Yoon, K. (1981). Multiple attribute decision-making methods and applications, a state-of-the-art survey. (Third Ed.). New York: Springer.
19) Jiang, P., Wang, W., Hu, Y.-C., Chiu, Y.-J., & Tsao, S.J. (2021). Pattern classification using tolerance rough sets based on non-additive grey relational analysis and DEMATEL. Grey Systems: Theory and Applications, 11(1), 169–182. https://doi.org/10.1108/GS-05-2020-0062
20) Kao, Ch., & Liu, Sh. (2019). Cross efficiency measurement and decomposition in two basic network systems. Omega,83, 70-79. https://doi.org/10.1016/j.omega.2018.02.004
21) Karimi, T. & Ahmadian, M. (2024), Grey clustering and grey ranking of bank branches based on grey efficiency, Grey Systems: Theory and Application, 14(1), 1-20. https://doi.org/10.1108/GS-04-2023-0034
22) Kuo, Y., Yang, T., & Huang, G.W. (2008). The use of a grey-based Taguchi method for optimizing multi response simulation problems. Engineering Optimization, 40, 517– 528.
23) Kuo, Y., Yang, T., & Huang, G.W. (2008). The use of grey relational analysis in solving multiple attribute decision-making problems. Computers and Industrial Engineering, 55, 80–93.
24) Lassila, J., Viljainen, S., Honkapuro, S., & Partanen, J. (2003). Data Envelopment Analysis in the benchmarking of electricity distribution companies. 17th International Conference on Electricity Distribution, Barcelona, Spain.
25) Lin, S.J., Lu, I.J., & Lewis, L. (2007). Grey relation performance correlations among economics, energy use and carbon dioxide emission in Taiwan, Energy Policy, 35, 1948-1955.
26) Lin, Y., Chen, M., & Liu, S. (2004). Theory of grey system information. Kybernetes, 33 (2), 196-218.
27) Linkov, I., & Moberg, E. Multi-criteria decision analysis environmental applications and case studies. USA: CRS Press; 2012.
28) Liu, J.S., Lu, L.Y.Y., Lu, W., & Lin, B.J.Y. (2013). A survey of DEA applications. Omega, 41 (5), 893-902.
29) Liu, W., Zhou, Zh., Ma, Ch., Liu, D., & Shen, W. (2015). Two-stage DEA models with undesirable input-intermediate-outputs. Omega, 56, 74-87.
30) Maghbouli, M., Amirteimoori, A., & Kordrostami, S. (2014). Two-stage network structures with undesirable outputs: A DEA based approach. Measurement, 48, 109-118.
31) Ministry of Energy. (2012). History. Retrieved from https://moe.gov.ir/%D9%88%D8%B2%D8%A7%D8%B1%D8%AA-%D9%86%D9%8A%D8%B1%D9%88/%D8%AA%D8%A7%D8%B1%D9%8A%D8%AE%DA%86%D9%87?lang=en-us
32) Neely, A., Adams, C., & Kennerley, M. (2002). The performance prism: the scorecard for measuring and managing business success. London: Financial Times Prentice Hall.
33) Nguyen, N. (2020). Integrating Data Envelopment Analysis and Grey System Theory to Access Ho Chi Minh City Power Corporation Performance Analysis. Journal of Southwest Jiaotong University, 55(2), https://doi.org/10.35741/issn.0258-2724.55.2.64.
34) Omrani, H., Gharizadeh Beiragh, R., & Shafiei Kaleibari, S. (2015). Performance assessment of Iranian electricity distribution companies by an integrated cooperative game data envelopment analysis principal component analysis approach. Electrical Power and Energy Systems, 64, 617-625.
35) Patyal, V.S., Kumar, R., Lamba, K., & Maheshwari, S. (2023). Performance evaluation of Indian electricity distribution companies: An integrated DEA-IRP-TOPSIS approach. Energy Economics, 124, 106796.
36) Sarraf, S., & HashemiNejad, Sh. (2020). Improving performance evaluation based on balanced scorecard with grey relational analysis and data envelopment analysis approaches: Case study in water and wastewater companies. Evaluation and Program Planning, 79, 101762. https://doi.org/10.1016/j.evalprogplan.2019.101762
37) Shannon, C.E. (1948). A mathematical theory of communication. Bell Systems Technical Journal, 27, 379–423.
38) Susanty, A., Purwanggono, B., & Al Faruq, C. (2022). Electricity distribution efficiency analysis using data envelopment analysis (DEA) and soft system methodology. Procedia Computer Science, 203, 342–349. https://doi.org/10.1016/j.procs.2022.07.043
39) Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197(1), 243-252.
40) Tone, K., & Tsutsui, M. (2010). Dynamic DEA: A slacks-based measure approach. Omega, 38(3-4), 145-156.
41) Wang, M., Huang, Y., & Li, D. (2021). Assessing the performance of industrial water resource utilization systems in China based on a two-stage DEA approach with game cross efficiency. Journal of Cleaner Production, 312, 127722.
42) Yoon, K.P., & Hwang, C.L. (1995). Multiple Attribute Decision Making: An Introduction. London: Sage Publication.
43) Zuo, Z., Guo, H., Li, Y., & Cheng, J. (2022). A two-stage DEA evaluation of Chinese mining industry technological innovation efficiency and eco-efficiency. Environmental Impact Assessment Review, 94, 106762. https://doi.org/10.1016/j.eiar.2022.106762
44) Zuo, Z., Guo, H., Li, Y., & Cheng, J. (2022). A two-stage DEA evaluation of Chinese mining industry technological innovation efficiency and eco-efficiency. Environmental Impact Assessment, 94, 106762. https://doi.org/10.1016/j.eiar.2022.106762
1) Bachchhav, B., Bharne, S., Choudhari, A., & Pattanshetti, S. (2023). Selection of spot welding electrode material by AHP, TOPSIS, and SAW. Materials Today: Proceedings,2023. https://doi.org/10.1016/j.matpr.2023.02.253.
2) Celen, A. (2013). Efficiency and productivity (TFP) of the Turkish electricity distribution companies: An application of two-stage (DEA & Tobit) analysis. Energy Policy, 63, 300-310.
3) Charnes, A., & Cooper, W.W. (1985). Preface to topics in data envelopment analysis. Annals of Operational Research, 2, 59- 70.
4) Charnes, A., Cooper, W.W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2, 429-444.
5) Chia-Nan Wang, Ch, N., Dang, T.T., Nguyen, A., N., T. & Wang, J.W. (2022). A combined Data Envelopment Analysis (DEA) and Grey Based Multiple Criteria Decision Making (G-MCDM) for solar PV power plants site selection: A case study in Vietnam. Energy Reports, 8, 1124-1142.
6) Chiang, B.C., Tsai, S.L., & Wang, C.C. (2002). Machine vision-based gray relational theory applied to IC marking inspection. IEEE Transactions on Semiconductor Manufacturing, 15 (4), 531-539.
7) Deng, J. (1989). Introduction to grey system theory. The Journal of Grey Systems, 1, 1-24.
8) Deng, J.L. (1982). Control problems of grey system. Systems and Control Letters, 1, 288–294.
9) Deng, J.L. (1992). Grey units. The Journal of Grey Systems, 4(2), 13-29.
10) Ebrahimzadeh Shermeh, H., Darvishnia, R., & Alavidoost, M.H. Evaluating the Efficiency of Power Companies Using Data Envelopment Analysis Based on SBM Models: A Case Study in Power Industry of Iran. Journal of Applied Research on Industrial Engineering, 5(4), 286-295. https://doi.org/10.22105/jarie.2018.81051.
11) Färe, R. (1991). Measuring Farrell efficiency for a firm with intermediate inputs. Academia economic papers, 19(2), 329-340.
12) Färe, R., & Grosskopf, Sh. (2000). Theory and Application of Directional Distance Functions. Journal of Productivity Analysis, 13(2), 93 – 103.
13) Färe, R., Grosskopf, Sh., & Tyteca, D. (1996). An activity analysis model of the environmental performance of firms—application to fossil-fuel-fired electric utilities. Ecological Economics, 18(2), 161-175.https://doi.org/10.1016/0921-8009(96)00019-5.
14) Färe, R., & Whittaker, G. (1995). An Intermediate Input Model of Dairy Production Using Complex Survey Data. Journal of Agricultural Economist, 46(2), 201-213.
15) Fung, C. P. (2003). Manufacturing process optimization for wear property of fiber-reinforced poly butylene terephthalate composites with grey relational analysis. Wear, 254, 298– 306. https://doi.org/10.1016/S0043-1648(03)00013-9
16) Hezer, S., Gelmez, E., & Özceylan, E. (2021). Comparative analysis of TOPSIS, VIKOR and COPRAS methods for the COVID-19 Regional Safety Assessment. Journal of Infection and Public Health, 14(6), 775-786. https://doi.org/10.1016/j.jiph.2021.03.003
17) Hisa, K.H., Chen, M.Y., & Chang, M.C. (2004). Comments on data pre-processing for grey relational analysis. Journal of Grey System, 7(1), 15-20.
18) Hwang, C.L., & Yoon, K. (1981). Multiple attribute decision-making methods and applications, a state-of-the-art survey. (Third Ed.). New York: Springer.
19) Jiang, P., Wang, W., Hu, Y.-C., Chiu, Y.-J., & Tsao, S.J. (2021). Pattern classification using tolerance rough sets based on non-additive grey relational analysis and DEMATEL. Grey Systems: Theory and Applications, 11(1), 169–182. https://doi.org/10.1108/GS-05-2020-0062
20) Kao, Ch., & Liu, Sh. (2019). Cross efficiency measurement and decomposition in two basic network systems. Omega,83, 70-79. https://doi.org/10.1016/j.omega.2018.02.004
21) Karimi, T. & Ahmadian, M. (2024), Grey clustering and grey ranking of bank branches based on grey efficiency, Grey Systems: Theory and Application, 14(1), 1-20. https://doi.org/10.1108/GS-04-2023-0034
22) Kuo, Y., Yang, T., & Huang, G.W. (2008). The use of a grey-based Taguchi method for optimizing multi response simulation problems. Engineering Optimization, 40, 517– 528.
23) Kuo, Y., Yang, T., & Huang, G.W. (2008). The use of grey relational analysis in solving multiple attribute decision-making problems. Computers and Industrial Engineering, 55, 80–93.
24) Lassila, J., Viljainen, S., Honkapuro, S., & Partanen, J. (2003). Data Envelopment Analysis in the benchmarking of electricity distribution companies. 17th International Conference on Electricity Distribution, Barcelona, Spain.
25) Lin, S.J., Lu, I.J., & Lewis, L. (2007). Grey relation performance correlations among economics, energy use and carbon dioxide emission in Taiwan, Energy Policy, 35, 1948-1955.
26) Lin, Y., Chen, M., & Liu, S. (2004). Theory of grey system information. Kybernetes, 33 (2), 196-218.
27) Linkov, I., & Moberg, E. Multi-criteria decision analysis environmental applications and case studies. USA: CRS Press; 2012.
28) Liu, J.S., Lu, L.Y.Y., Lu, W., & Lin, B.J.Y. (2013). A survey of DEA applications. Omega, 41 (5), 893-902.
29) Liu, W., Zhou, Zh., Ma, Ch., Liu, D., & Shen, W. (2015). Two-stage DEA models with undesirable input-intermediate-outputs. Omega, 56, 74-87.
30) Maghbouli, M., Amirteimoori, A., & Kordrostami, S. (2014). Two-stage network structures with undesirable outputs: A DEA based approach. Measurement, 48, 109-118.
31) Ministry of Energy. (2012). History. Retrieved from https://moe.gov.ir/%D9%88%D8%B2%D8%A7%D8%B1%D8%AA-%D9%86%D9%8A%D8%B1%D9%88/%D8%AA%D8%A7%D8%B1%D9%8A%D8%AE%DA%86%D9%87?lang=en-us
32) Neely, A., Adams, C., & Kennerley, M. (2002). The performance prism: the scorecard for measuring and managing business success. London: Financial Times Prentice Hall.
33) Nguyen, N. (2020). Integrating Data Envelopment Analysis and Grey System Theory to Access Ho Chi Minh City Power Corporation Performance Analysis. Journal of Southwest Jiaotong University, 55(2), https://doi.org/10.35741/issn.0258-2724.55.2.64.
34) Omrani, H., Gharizadeh Beiragh, R., & Shafiei Kaleibari, S. (2015). Performance assessment of Iranian electricity distribution companies by an integrated cooperative game data envelopment analysis principal component analysis approach. Electrical Power and Energy Systems, 64, 617-625.
35) Patyal, V.S., Kumar, R., Lamba, K., & Maheshwari, S. (2023). Performance evaluation of Indian electricity distribution companies: An integrated DEA-IRP-TOPSIS approach. Energy Economics, 124, 106796.
36) Sarraf, S., & HashemiNejad, Sh. (2020). Improving performance evaluation based on balanced scorecard with grey relational analysis and data envelopment analysis approaches: Case study in water and wastewater companies. Evaluation and Program Planning, 79, 101762. https://doi.org/10.1016/j.evalprogplan.2019.101762
37) Shannon, C.E. (1948). A mathematical theory of communication. Bell Systems Technical Journal, 27, 379–423.
38) Susanty, A., Purwanggono, B., & Al Faruq, C. (2022). Electricity distribution efficiency analysis using data envelopment analysis (DEA) and soft system methodology. Procedia Computer Science, 203, 342–349. https://doi.org/10.1016/j.procs.2022.07.043
39) Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197(1), 243-252.
40) Tone, K., & Tsutsui, M. (2010). Dynamic DEA: A slacks-based measure approach. Omega, 38(3-4), 145-156.
41) Wang, M., Huang, Y., & Li, D. (2021). Assessing the performance of industrial water resource utilization systems in China based on a two-stage DEA approach with game cross efficiency. Journal of Cleaner Production, 312, 127722.
42) Yoon, K.P., & Hwang, C.L. (1995). Multiple Attribute Decision Making: An Introduction. London: Sage Publication.
43) Zuo, Z., Guo, H., Li, Y., & Cheng, J. (2022). A two-stage DEA evaluation of Chinese mining industry technological innovation efficiency and eco-efficiency. Environmental Impact Assessment Review, 94, 106762. https://doi.org/10.1016/j.eiar.2022.106762
44) Zuo, Z., Guo, H., Li, Y., & Cheng, J. (2022). A two-stage DEA evaluation of Chinese mining industry technological innovation efficiency and eco-efficiency. Environmental Impact Assessment, 94, 106762. https://doi.org/10.1016/j.eiar.2022.106762
