A Review of Inverse Data Envelopment Analysis: Origins, Development, and Future Directions

Emrouznejad, A., G. R. Amin, M. Ghiyasi, M. Michali (2023) A Review of Inverse Data Envelopment Analysis: Origins, Development, and Future Directions, IMA Journal of Management Mathematics, https://doi.org/10.1093/imaman/dpad006.

Please wait while flipbook is loading. For more related info, FAQs and issues please refer to DearFlip WordPress Flipbook Plugin Help documentation.

 

List of papers on Inverse Data Envelopment Analysis

 

  • Adimi, M. E., Malkhalife, M. R., Lotfi, F. H., and Mehrjoo, R. (2020). Features of the efficiency frontier and its application in Inverse DEA without solving a model. Journal of Mathematical Extension, 14(1), 203-220.
  • Ahuja, R. K., and Orlin, J. B. (2001). Inverse optimization. Operations Research, 49(5), 771-783.
  • Amin, G. R., and Al-Muharrami, S. (2018). A new inverse data envelopment analysis model for mergers with negative data. IMA Journal of Management Mathematics, 29(2), 137-149.
  • Amin, G. R., Al-Muharrami, S., and Toloo, M. (2019a). A combined goal programming and inverse DEA method for target setting in mergers. Expert Systems with Applications, 115, 412-417.
  • Amin, G. R., and Emrouznejad, A. (2007). Inverse forecasting: A new approach for predictive modeling. Computers Industrial Engineering, 53(3), 491-498.
  • Amin, G. R., Emrouznejad, A., and Gattoufi, S. (2017a). Minor and major consolidations in inverse DEA: Definition and determination. Computers and Industrial Engineering, 103, 193-200. doi:10.1016/j.cie.2016.11.029
  • Amin, G. R., Emrouznejad, A., and Gattoufi, S. (2017b). Modelling generalized firms’ restructuring using inverse DEA. Journal of Productivity Analysis, 48(1), 51-61. doi:10.1007/s11123-017-0501-y
  • Amin, G. R., and Ibn-Boamah, M. (2020). A new inverse DEA cost efficiency model for estimating potential merger gains: a case of Canadian banks. Annals of Operations Research, 1-16.
  • Amin, G. R., and Ibn-Boamah, M. (2021). A two-stage inverse data envelopment analysis approach for estimating potential merger gains in the US banking sector. Managerial and Decision Economics, 42 (6), 1454-1465.
  • Amin, G. R., and Ibn-Boamah, M. (2023). Modeling business partnerships: a data envelopment analysis approach. European Journal of Operational Research,305(1), 329-337.
  • Amin, G. R., and Oukil, A. (2019b). Flexible target setting in mergers using inverse data envelopment analysis. International Journal of Operational Research, 35(3), 301-317.
  • An, Q., Liu, X., Li, Y., and Xiong, B. (2019). Resource planning of Chinese commercial banking systems using two-stage inverse data envelopment analysis with undesirable outputs. PLoS One, 14(6), e0218214.
  • Avand, M., Ghobadi, S., Jahangiri, S. (2023). A new inverse DEA model for units  restructuring: A case study of commercial banks members of the Persian Gulf Corporation Council (GCC), Sharif Journal of Industrial Engineering and Management.
  • Çakır, S. (2017). Proposing integrated Shannon’s entropy–inverse data envelopment analysis methods for resource allocation problem under a fuzzy environment. Engineering Optimization, 49(10), 1733-1749.
  • Charnes, A., Cooper, W., and Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
  • Chen, L., Gao, Y., Li, M.-J., Wang, Y.-M., and Liao, L.-H. (2021). A new inverse data envelopment analysis approach to achieve China’s road transportation safety objectives. Safety Science, 142, 105362.
  • Chen, L., and Wang, Y.-M. (2021). Limitation and optimization of inputs and outputs in the inverse data envelopment analysis under variable returns to scale. Expert Systems with Applications, 115344.
  • Chen, L., Wang, Y., Lai, F., and Feng, F. (2017). An investment analysis for China’s sustainable development based on inverse data envelopment analysis. Journal of Cleaner Production, 142, 1638-1649.
  • Emrouznejad, A., Anouze, A. L., and Thanassoulis, E. (2010). A semi-oriented radial measure for measuring the efficiency of decision making units with negative data, using DEA. European Journal of Operational Research, 200(1), 297-304.
  • Emrouznejad, A., and Yang, G.-l. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4-8.
  • Emrouznejad, A., Yang, G.-l., and Amin, G. R. (2019). A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in Chinese manufacturing industries. Journal of the Operational Research Society, 70(7), 1079-1090. doi:10.1080/01605682.2018.1489344
  • Foladi, S., Solimanpur, M., and Jahangoshai Rezaee, M. (2020). Inverse Dynamic Data Envelopment Analysis for Evaluating Faculties of University with Quasi-Fixed Inputs. Social Indicators Research, 148(1), 323-347. doi:10.1007/s11205-019-02196-8
  • Gatimbu, K. K., Ogada, M. J., and Budambula, N. L. (2019). Environmental efficiency of small-scale tea processors in Kenya: an inverse data envelopment analysis (DEA) approach. Environment, Development Sustainability (Switzerland), 1-13.
  • Gattoufi, S., Amin, G. R., and Emrouznejad, A. (2014). A new inverse DEA method for merging banks. IMA Journal of Management Mathematics, 25(1), 73-87. doi:10.1093/imaman/dps027.
  • Gerami, J., Mozaffari, M.R., Wanke, P.F., and Correa, H.L. (2023). A generalized inverse DEA model for firm restructuring based on value efficiency. IMA Journal of Management Mathematics, https://doi.org/10.1093/imaman/dpab043.
  • Gharibi, K. and Abdollahzadeh, S. (2021), “A mixed-integer linear programming approach for circular economy-led closed-loop supply chains in green reverse logistics network design under uncertainty”, Journal of Enterprise Information Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JEIM-11-2020-0472
  • Ghiyasi, M. (2015). On inverse DEA model: The case of variable returns to scale. Computers and Industrial Engineering, 87, 407-409. doi:10.1016/j.cie.2015.05.018
  • Ghiyasi, M. (2017a). Industrial sector environmental planning and energy efficiency of Iranian provinces. Journal of Cleaner Production, 142, 2328-2339. doi:10.1016/j.jclepro.2016.11.044
  • Ghiyasi, M. (2017b). Inverse DEA based on cost and revenue efficiency. Computers and Industrial Engineering, 114, 258-263. doi:10.1016/j.cie.2017.10.024
  • Ghiyasi, M. (2019). Emission utilization permission based on environmental efficiency analysis. Environmental Science Pollution Research, 26(21), 21295-21303.
  • Ghiyasi, M. (2022). Environmental efficiency and sensitivity analysis of Industrial sectors for Iranian provinces: A time series-like inverse DEA based approach. Environment, Development and Sustainability
  • Ghiyasi, M., Soltanifar, M. and Sharafi, H. (2022). A novel inverse DEA-R model with application in hospital efficiency. Socio-Economic Planning Sciences. 84. 101427.
  • Ghiyasi, M., and Zhu, N. (2020). An inverse semi-oriented radial data envelopment analysis measure for dealing with negative data. IMA Journal of Management Mathematics, 31(4), 505-516.
  • Ghomia, A., Ghobadib, S., Behzadia, M. H., and Rostamy-Malkhalifehc, M. (2021). Inverse data envelopment analysis with stochastic data. RAIRO – Operations Research, 55, 2739-2762.
  • Guijarro, F., Martínez-Gómez, M., and Visbal-Cadavid, D. (2020). A model for sector restructuring through genetic algorithm and inverse DEA. Expert Systems with Applications, 154. doi:10.1016/j.eswa.2020.113422
  • Hadi-Vencheh, A., Foroughi, A. A., and Soleimani-damaneh, M. (2008). A DEA model for resource allocation. Economic Modelling, 25(5), 983-993. doi:10.1016/j.econmod.2008.01.003
  • Hassanzadeh, A., Yousefi, S., Saen, R. F., and Hosseininia, S. S. S. (2018). How to assess sustainability of countries via inverse data envelopment analysis? Clean Technologies Environmental Policy, 20(1), 29-40.
  • He, X., Chen, L., and Huang, Y. (2022).A Study of Forest Carbon Sink Increment from the Perspective of Efficiency Evaluation Based on an Inverse DEA Model. Forests, 13(10), 1563. https://doi.org/10.3390/f13101563
  • Hosseininia, S. S., and Saen, R. F. (2020). Developing a novel inverse data envelopment analysis (DEA) model for evaluating after‐sales units. Expert Systems, e12579.
  • Hu, X. Y., Li, J. S., Li, X. Y., and Cui, J. C. (2020). Carbon Emissions Abatement (CEA) Allocation Based on Inverse Slack-Based Model (SBM). Journal of the Operations Research Society of China, 9, 475-498. doi:10.1007/s40305-020-00303-y
  • Huang, S., and Liu, Z. (1999). On the inverse problem of linear programming and its application to minimum weight perfect k-matching. European Journal of Operational Research, 112(2), 421-426.
  • Jahanshahloo, G. R., Soleimani-Damaneh, M., and Ghobadi, S. (2015). Inverse DEA under inter-temporal dependence using multiple-objective programming. European Journal of Operational Research, 240(2), 447-456. doi:10.1016/j.ejor.2014.07.002
  • Kalantary, M., Farzipoor Saen, R., and Toloie Eshlaghy, A. (2018). Sustainability assessment of supply chains by inverse network dynamic data envelopment analysis. Scientia Iranica, 25(6), 3723-3743.
  • Kalantary, M., and Saen, R. F. (2019). Assessing sustainability of supply chains: An inverse network dynamic DEA model. Computers Industrial Engineering, 135, 1224-1238.
  • Kazemi, A., and Galagedera, D.U.A. (2023). An inverse DEA model for intermediate and output target setting in serially linked general two-stage processes. IMA Journal of Management Mathematics, https://doi.org/10.1093/imaman/dpab041.
  • Le, M. H., Afsharian, M., and Ahn, H. (2021). Inverse Frontier-based Benchmarking for Investigating the Efficiency and Achieving the Targets in the Vietnamese Education System. Omega, 102427.
  • Lertworasirikul, S., Charnsethikul, P., and Fang, S.-C. (2011). Inverse data envelopment analysis model to preserve relative efficiency values: The case of variable returns to scale. Computers Industrial Engineering, 61(4), 1017-1023.
  • Lim, D.-J. (2016). Inverse DEA with frontier changes for new product target setting. European journal of operational research, 254(2), 510-516.doi:10.1016/j.ejor.2016.03.059.
  • Lim, D.-J. (2020). Inverse data envelopment analysis for operational planning: The impact of oil price shocks on the production frontier. Expert Systems with Applications, 161, 113726. doi:10.1016/j.ejor.2016.03.059
  • Lin, H.-T. (2010). An efficiency-driven approach for setting revenue target. Decision Support Systems, 49(3), 311-317.
  • Lin, Y., Yan, L., and Wang, Y.-M. (2019). Performance evaluation and investment analysis for container port sustainable development in china: An inverse DEA approach. Sustainability (Switzerland), 11(17), 4617.
  • Mahla, D., Agarwal, S., Amin, G.R., and Mathur, T. (2023). An inverse data envelopment analysis model to consider ratio data and preferences of decision-makers. IMA Journal of Management Mathematics, https://doi.org/10.1093/imaman/dpac009.
  • Modhej, D., Sanei, M., Shoja, N., and HosseinzadehLotfi, F. (2017). Integrating inverse data envelopment analysis and neural network to preserve relative efficiency values. Journal of Intelligent Fuzzy Systems, 32(6), 4047-4058.
  • Moghaddas, Z., Tosarkani, B. M., and Yousefi, S. (2022). Resource reallocation for improving sustainable supply chain performance: An inverse data envelopment analysis. International Journal of Production Economics, In Press, 108560.
  • Orisaremi, K. K., Chan, F. T., and Chung, N. S. (2021). Potential reductions in global gas flaring for determining the optimal sizing of gas-to-wire (GTW) process: An inverse DEA approach. Journal of Natural Gas Science and Engineering, 93, 103995.
  • Orisaremi, K.K., Chan, F.T.S., Chung, S.H. and Fu, X.W. (2022). A sustainable lean production framework based on inverse DEA for mitigating gas flaring. Expert Syst. Appl., 206, 117856.
  • Oukil, A. (2023). Investigating prospective gains from mergers in the agricultural sector through Inverse DEA. IMA Journal of Management Mathematics, https://doi.org/10.1093/imaman/dpac004.
  • Oukil, A., Nourani, A., Bencheikh, A., Soltani, A.A. (2022). Using inverse data envelopment analysis to evaluate potential impact of mergers on energy use optimization – Application in the agricultural production, Journal of Cleaner Production, 381, Part 1.
  • Saen, R. F., and Nia, S. S. S. H. (2019). Evaluating after-sales service units by developing inverse network data envelopment analysis model. Benchmarking: An International Journal.
  • Shiri Daryani, Z., Tohidi, G., Daneshian, B., Razavyan, S. and Hosseinzadeh Lotfi, F. (2021). Inverse DEA in two-stage systems based on allocative efficiency. Journal of Intelligent and Fuzzy Systems, 40(1), 591-603.
  • Soltanifar, M., Ghiyasi, M., and Sharafi, H. (2023). Inverse DEA-R models for merger analysis with negative data. IMA Journal of Management Mathematics, https://doi.org/10.1093/imaman/dpac001.
  • Wegener, M., and Amin, G. R. (2019). Minimizing greenhouse gas emissions using inverse DEA with an application in oil and gas. Expert Systems with Applications, 122, 369-375.
  • Wei, Q., Zhang, J., and Zhang, X. (2000). An inverse DEA model for inputs/outputs estimate. European Journal of Operational Research, 121(1), 151-163.
  • Yan, H., Wei, Q., and Hao, G. (2002). DEA models for resource reallocation and production input/output estimation. European Journal of Operational Research, 136(1), 19-31. doi:10.1016/S0377-2217(01)00046-7
  • Younesi, A., Lotfi, F.H. and Arana-Jiménez, M. (2023). Using slacks-based model to solve inverse DEA with integer intervals for input estimation. Fuzzy Optim Decis Making. https://doi.org/10.1007/s10700-022-09403-1
  • Yousefi, S., Hassanzadeh, A., Saen, R. F., and Kashi, Z. M. (2021). Assessing sustainability of Islamic countries via data envelopment analysis (DEA). Clean Technologies Environmental Policy, 24, 1129-1143.
  • Yu, A., Shao, Y., You, J., Wu, M., and Xu, T. (2019). Estimations of operational efficiencies and potential income gains considering the credit risk for China’s banks. Journal of the Operational Research Society, 70(12), 2153-2168.
  • Zeinodin, E., and Ghobadi, S. (2019). Merging DMUs Based on of the Idea Inverse DEA. Iranian Journal of Optimization, 11(2), 77-84.
  • Zhang, G., and Cui, J. (2020). A general inverse DEA model for non-radial DEA. Computers and Industrial Engineering, 142. doi:10.1016/j.cie.2020.106368
  • Zhang, J., Jin, W., Yang, G.-l., Li, H., Ke, Y., and Philbin, S. P. (2021). Optimizing regional allocation of CO2 emissions considering output under overall efficiency. Socio-economic planning sciences, Socio-Economic Planning Sciences, 101012.
  • Zhang, J., and Liu, Z. (1996). Calculating some inverse linear programming problems. Journal of Computational and Applied Mathematics, 72(2), 261-273.
  • Zhang, J., and Liu, Z. (1999). A further study on inverse linear programming problems. Journal of Computational and Applied Mathematics, 106(2), 345-359.
  • Zhang, M., and Cui, J. C. (2016). The extension and integration of the inverse DEA method. Journal of the Operational Research Society, 67(9), 1212-1220. doi:10.1057/jors.2016.2
  • Zhang, X. S., and Cui, J. C. (1999). A project evaluation system in the state economic information system of China an operations research practice in public sectors. International Transactions in Operational Research, 6(5), 441-452.