Subject Areas : International Journal of Data Envelopment Analysis
1 - Department of Mathematics, Shiraz branch, Islamic Azad University, Shiraz, Iran
Keywords:
Abstract :
References:
[1] Pastor J, Ruiz J. Variables with negative values in DEA. In Zhu, J., Cook, W.D. (Eds.) Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis. (Springer, NY). 2007 63-84.
[2] Banker RD, Charnes A, Cooper WW. Some models for estimating technical and scale efficiencies in data envelopment analysis. Management Science. 1984 30: 1078-1092.
[3] Seiford LM, Zhu J. Modeling undesirable factors in efficiency evaluation, European Journal of Operational Research, 2002 142: 16-20.
[4] Scheel H. Undesirable outputs in efficiency valuations. European Journal of Operational Research. 2001 132: 400-410.
[5] Halme M, Joro T, Koivu M. Dealing with interval scale data in data envelopment analysis. European Journal of Operational Research. 2002 137: 22-27.
[6] Emrouznejad A, Amin GR, Thanassoulis E, Anouze AL. On the boundedness of the SORM DEA models with negative data. European Journal of Operational Research. 2010 206: 265-268.
[7] Emrouznejad A, Anouze AL, Thanassoulis E. A semi-oriented radial measure for measuring the efficiency of decision making units with negative data, using DEA. European Journal of Operational Research. 2010 200: 297-304.
[8] Mehdiloozad M, Zhu J, Sahoo BK. Identification of congestion in data envelopment analysis under the occurrence of multiple projections: A reliable method capable of dealing with negative data. European Journal of Operational Research. 2018 265: 644-654.
[9] Sharp JA, Meng W, Liu W. A modified slacks-based measure model for data envelopment analysis with „natural‟ negative outputs and inputs, Journal of the Operational Research Society. 2007 58: 1672-1677.
[10] Aparicio J, Ruiz JL, Sirvent I. Closest targets and minimum distance to the Pareto-efficient frontier in DEA. Journal of Productivity Analysis. 2007 28: 209-218.
[11] Lin R, Yang W, Huang H. A modified slacks-based super-efficiency measure in the presence of negative data. Computers & Industrial Engineering. 2019 135: 39-52.
[12] Lo SF, Lu WM. An integrated performance evaluation of financial holding companies in Taiwan. European Journal of Operational Research. 2009 198: 341-350.
[13] Lin R, Liu Y. Super-efficiency based on the directional distance function in the presence of negative data. Omega 2019 85: 26-34.
[14] Portela MCAS, Thanassoulis E, Simpson G. Negative data in DEA: A directional distance approach applied to bank branches. Journal of the Operational Research Society. 2004 55: 1111-1121.
[15] Tavana M, Izadikhah M, Di Caprio, D, Saen RF. A new dynamic range directional measure for two-stage data envelopment analysis models with negative data. Computers & Industrial Engineering. 2018 115: 427-448.
[16] Cheng G, Zervopoulos P, Qian Z. A variant of radial measure capable of dealing with negative inputs and outputs in data envelopment analysis. European Journal of Operational Research. 2013 225: 100-105.
[17] Zhang X-S, Cui J-C. A project evaluation system in the state economic information system of China: An operations research practice in public sectors, International Transactions in Operational Research, 1999 6: 441–452.
[18] Wei Q, Zhang J, Zhang X. An inverse DEA model for inputs/outputs estimate. European Journal of Operational Research. 2000 121(1): 151–163.
[19] Hadi-Vencheh A, Foroughi AA, Soleimani-damaneh M. A DEA model for resource allocation, Economic Modelling. 2008 25(5): 983–993.
[20] Zhang M, Wang L-L, Cui J-C. Extra resource allocation: A DEA approach in the view of efficiencies, Journal of the Operations Research Society of China. 2018 6(1): 85–106.
[21] Jahanshahloo GR, Soleimani-damaneh M, Ghobadi S. Inverse DEA under inter-temporal dependence using multiple-objective programming., European Journal of Operational Research. 2015 240(2): 447–456.
[22] Ghobadi S, Jahangiri S. Inverse DEA: Review, extension and application. International Journal of Information Technology & Decision Making. 2015 14(04): 805–824.
[23] Lim, D.-J., 2016. Inverse DEA with frontier changes for new product target setting, European Journal of Operational Research, 254(2), 510–516.
[24] Eyni M, Tohidi G, Mehrabeian S. Applying inverse DEA and cone constraint to sensitivity analysis of DMUs with undesirable inputs and outputs. Journal of the Operational Research Society. 2017 68(1): 34–40.
[25] Ghiyasi M. Inverse DEA based on cost and revenue efficiency. Computers & Industrial Engineering., 2017 114: 258–263.
[26] Hassanzadeh A, Yousefi S, Saen R F, Hosseininia SSS. How to assess sustainability of countries via inverse data envelopment analysis? Clean Technologies and Environmental Policy. 2018 20(1): 29–40.
[27] Amin GR, Emrouznejad A, Gattouf S. Modelling generalized firms’ restructuring using inverse DEA. Journal of Productivity Analysis. 2017 48(1): 51–61.
[28] Gattoufi S, Amin GR, Emrouznejad A. A new inverse DEA method for merging banks. IMA Journal of Management Mathematics. 2014 25: 73–87.
[29] Emrouznejad A, Yang G, Amin G R. A novel inverse DEA model with application to allocate the 〖CO〗_2 emissions quota to different regions in Chinese manufacturing industries. Journal of the Operational Research Society. 2019 70(7): 1079–1090.
[30] Wegener M, Amin GR. Minimizing GHG emissions using inverse DEA with an application in oil and gas. Expert Systems with Applications. 2019 122, 369–375.
[31] Gerami J, Mozaffari MR, Wanke PF, Correa HL. A generalized inverse DEA model for firm restructuring based on value efficiency, IMA Journal of Management Mathematics, 2023 34(3): 541–580.
[32] Gerami J, Mozaffari MR, Wanke PF. A multi-criteria ratio-based approach for two-stage data envelopment analysis. Expert Systems with Applications. 2020 158: 113508.
[33] Gerami J, Kiani Mavi R, Farzipoor Saen R, Kiani Mavi N. A novel network DEA-R model for evaluating hospital services supply chain performance. Annals of Operations Research. 2020, 324, 1–2: 1041–1066.
[34] Gerami J. An interactive procedure to improve estimate of value efficiency in DEA. Expert Systems with Applications. 15 December 2019, 137 29-45.
[35] Gerami J, Mozaffari MR, Wanke PF, Correa H. A novel slacks-based model for efficiency and super-efficiency in DEA-R. Operations Research. 2021 22, 4: 3373–3410.
[36] Gerami J. Strategic alliances and partnerships based on the semi-additive production technology in DEA. Expert Systems with Applications. 1 October 2024 251: 123986.
[1] Pastor J, Ruiz J. Variables with negative values in DEA. In Zhu, J., Cook, W.D. (Eds.) Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis. (Springer, NY). 2007 63-84.
[2] Banker RD, Charnes A, Cooper WW. Some models for estimating technical and scale efficiencies in data envelopment analysis. Management Science. 1984 30: 1078-1092.
[3] Seiford LM, Zhu J. Modeling undesirable factors in efficiency evaluation, European Journal of Operational Research, 2002 142: 16-20.
[4] Scheel H. Undesirable outputs in efficiency valuations. European Journal of Operational Research. 2001 132: 400-410.
[5] Halme M, Joro T, Koivu M. Dealing with interval scale data in data envelopment analysis. European Journal of Operational Research. 2002 137: 22-27.
[6] Emrouznejad A, Amin GR, Thanassoulis E, Anouze AL. On the boundedness of the SORM DEA models with negative data. European Journal of Operational Research. 2010 206: 265-268.
[7] Emrouznejad A, Anouze AL, Thanassoulis E. A semi-oriented radial measure for measuring the efficiency of decision making units with negative data, using DEA. European Journal of Operational Research. 2010 200: 297-304.
[8] Mehdiloozad M, Zhu J, Sahoo BK. Identification of congestion in data envelopment analysis under the occurrence of multiple projections: A reliable method capable of dealing with negative data. European Journal of Operational Research. 2018 265: 644-654.
[9] Sharp JA, Meng W, Liu W. A modified slacks-based measure model for data envelopment analysis with „natural‟ negative outputs and inputs, Journal of the Operational Research Society. 2007 58: 1672-1677.
[10] Aparicio J, Ruiz JL, Sirvent I. Closest targets and minimum distance to the Pareto-efficient frontier in DEA. Journal of Productivity Analysis. 2007 28: 209-218.
[11] Lin R, Yang W, Huang H. A modified slacks-based super-efficiency measure in the presence of negative data. Computers & Industrial Engineering. 2019 135: 39-52.
[12] Lo SF, Lu WM. An integrated performance evaluation of financial holding companies in Taiwan. European Journal of Operational Research. 2009 198: 341-350.
[13] Lin R, Liu Y. Super-efficiency based on the directional distance function in the presence of negative data. Omega 2019 85: 26-34.
[14] Portela MCAS, Thanassoulis E, Simpson G. Negative data in DEA: A directional distance approach applied to bank branches. Journal of the Operational Research Society. 2004 55: 1111-1121.
[15] Tavana M, Izadikhah M, Di Caprio, D, Saen RF. A new dynamic range directional measure for two-stage data envelopment analysis models with negative data. Computers & Industrial Engineering. 2018 115: 427-448.
[16] Cheng G, Zervopoulos P, Qian Z. A variant of radial measure capable of dealing with negative inputs and outputs in data envelopment analysis. European Journal of Operational Research. 2013 225: 100-105.
[17] Zhang X-S, Cui J-C. A project evaluation system in the state economic information system of China: An operations research practice in public sectors, International Transactions in Operational Research, 1999 6: 441–452.
[18] Wei Q, Zhang J, Zhang X. An inverse DEA model for inputs/outputs estimate. European Journal of Operational Research. 2000 121(1): 151–163.
[19] Hadi-Vencheh A, Foroughi AA, Soleimani-damaneh M. A DEA model for resource allocation, Economic Modelling. 2008 25(5): 983–993.
[20] Zhang M, Wang L-L, Cui J-C. Extra resource allocation: A DEA approach in the view of efficiencies, Journal of the Operations Research Society of China. 2018 6(1): 85–106.
[21] Jahanshahloo GR, Soleimani-damaneh M, Ghobadi S. Inverse DEA under inter-temporal dependence using multiple-objective programming., European Journal of Operational Research. 2015 240(2): 447–456.
[22] Ghobadi S, Jahangiri S. Inverse DEA: Review, extension and application. International Journal of Information Technology & Decision Making. 2015 14(04): 805–824.
[23] Lim, D.-J., 2016. Inverse DEA with frontier changes for new product target setting, European Journal of Operational Research, 254(2), 510–516.
[24] Eyni M, Tohidi G, Mehrabeian S. Applying inverse DEA and cone constraint to sensitivity analysis of DMUs with undesirable inputs and outputs. Journal of the Operational Research Society. 2017 68(1): 34–40.
[25] Ghiyasi M. Inverse DEA based on cost and revenue efficiency. Computers & Industrial Engineering., 2017 114: 258–263.
[26] Hassanzadeh A, Yousefi S, Saen R F, Hosseininia SSS. How to assess sustainability of countries via inverse data envelopment analysis? Clean Technologies and Environmental Policy. 2018 20(1): 29–40.
[27] Amin GR, Emrouznejad A, Gattouf S. Modelling generalized firms’ restructuring using inverse DEA. Journal of Productivity Analysis. 2017 48(1): 51–61.
[28] Gattoufi S, Amin GR, Emrouznejad A. A new inverse DEA method for merging banks. IMA Journal of Management Mathematics. 2014 25: 73–87.
[29] Emrouznejad A, Yang G, Amin G R. A novel inverse DEA model with application to allocate the 〖CO〗_2 emissions quota to different regions in Chinese manufacturing industries. Journal of the Operational Research Society. 2019 70(7): 1079–1090.
[30] Wegener M, Amin GR. Minimizing GHG emissions using inverse DEA with an application in oil and gas. Expert Systems with Applications. 2019 122, 369–375.
[31] Gerami J, Mozaffari MR, Wanke PF, Correa HL. A generalized inverse DEA model for firm restructuring based on value efficiency, IMA Journal of Management Mathematics, 2023 34(3): 541–580.
[32] Gerami J, Mozaffari MR, Wanke PF. A multi-criteria ratio-based approach for two-stage data envelopment analysis. Expert Systems with Applications. 2020 158: 113508.
[33] Gerami J, Kiani Mavi R, Farzipoor Saen R, Kiani Mavi N. A novel network DEA-R model for evaluating hospital services supply chain performance. Annals of Operations Research. 2020, 324, 1–2: 1041–1066.
[34] Gerami J. An interactive procedure to improve estimate of value efficiency in DEA. Expert Systems with Applications. 15 December 2019, 137 29-45.
[35] Gerami J, Mozaffari MR, Wanke PF, Correa H. A novel slacks-based model for efficiency and super-efficiency in DEA-R. Operations Research. 2021 22, 4: 3373–3410.
[36] Gerami J. Strategic alliances and partnerships based on the semi-additive production technology in DEA. Expert Systems with Applications. 1 October 2024 251: 123986.
