Merging of units based on inverse data envelopment analysis

Document Type : Research Paper

Authors

1 Department of Mathematics, Khomeinishar branch, Islamic Azad University, Isfahan, Iran.

2 Department of Mathematics, Najafabad branch, Islamic Azad University, Najafabad, Iran.

Abstract

Inverse data envelopment analysis (InvDEA) is a remarkable and popular management tool. This paper deals with one application of this tool. In fact, the problem of the combination of the units is investigated in the presence of negative data. The problem of combining units refers to the fact that a set of units create a new unit based on synergy to improve their performance. We use multiple objective programming for this purpose and suggest new models based on predetermined conditions for the new unit. The proposed models estimate inputs and outputs simultaneously. Importance advantages of the proposed models are i) We can follow multiple goals in the problem of combining units because multiple objective programming is applied. ii) Models can simultaneously estimate the inputs and outputs of the combined unit. iii) Unlike the existing methods in the InvDEA-based merging literature, the negative data do not need to be transferred to positive data. Finally, a numerical example is used to explain and validate the model proposed in this paper.

Keywords

Main Subjects


[1] G. R. Amin and S. Al-Muharrami. A new inverse data envelopment analysis model for mergers with negative data. IMA Journal of Management Mathematics, 00:1–13, 2016.
[2] G. R. Amin, A. Emrouznejad, and S. Gattoufi. Modelling generalized firms’ restructuring using inverse dea. Journal of Productivity Analysis, 48:51–61, 2017.
[3] A. Amirteimoori, B. K. Sahoo, V. Charles, and S. Mehdizadeh. Stochastic Benchmarking. Springer International Publishing, Switzerland, 2021.
[4] L. Dong Joon. Inverse dea with frontier changes for new target setting. European Journal of Operational Research, 254:510–516, 2016.
[5] M. Ehrgott. Multicriteria optimization. Springer Berlin, 2005.
[6] A. Emrouznejad, A. L. Anouze, and E. Thanassoulis. 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, 2010.
[7] A. Emrouznejad and M. Tavana. Performance Measurement with Fuzzy Data Envelopment Analysis. Springer, 2014.
[8] A. Emrouznejad and G. Yang. A survey and analysis of the first 40 years of scholarly literature in dea: 1978￿-2016. Journal of Socio-Economic Planning Sciences, 61:4–8, 2018.
[9] S. Gattoufi, G. R. Amin, and A. Emrouznejad. A new inverse dea method for merging banks. IMA Journal of Management Mathematics, 25:73–87, 2014.
[10] M. Ghiyasi and N. Zhu. An inverse semi-oriented radial data envelopment analysis measure for dealing with negative data. IMA Journal of Management Mathematics, 31(4):505–516, 2020.
[11] S. Ghobadi. Inputs and outputs estimation in inverse dea. Iranian Journal of Optimization, 9(2):119–129, 2017.
[12] S. Ghobadi. A dynamic dea model for resource allocation. Int. J. of Mathematics in Operational Research, 17(1):50–77, 2020.
[13] S. Ghobadi. Merging decision-making units with interval data. RAIRO-Operations Research, 55:1605–1630, 2021.
[14] S. Ghobadi, Kh. Soleimani, and E. Zanboori. A novel inverse dea model for restructuring dmus with negative data. Int. J. of Operational Research, 46(1):118–132, 2023.
[15] S. Ghobadi, Kh. Soleimani, and E. Zanboori. Simultaneous estimation of input-output levels under improving efficiency level in an assessment window. Soft Computing, (In print):https://doi.org/10.1007/s00500–023–07878–7, 2023.
[16] A. Ghomi, S. Ghobadi, M. H. Behzadi, and M. Rostamy-Malkhalifeh. Inverse data envelopment analysis with stochastic data. RAIRO-Operations Research, 55(5):2739 – 2762, 2021.
[17] A. Hadi-Vencheh and A. A. Foroughi. A generalized dea model for inputs/outputs estimation. Mathematical and Computer Modelling, 43(5-6):447–457, 2006.
[18] A. Hadi-Vencheh, A. A. Foroughi, and M. Soleimani-Damaneh. A dea model for resource allocation. Economic Modelling, 25(5):983–993, 2008.
[19] G. R. Jahanshahloo, F. H. Lotfi, N. Shoja, G. Tohidi, and S. Razavyan. Sensitivity of efficiency classifications in the inverse dea models. Applied Mathematics and Computation, 169(2):905–916, 2005.
[20] C. A. K. Lovell. Measuring the macroeconomic performance of the taiwanese economy. Int. J. Prod. Econ., 39:165–178, 1995.
[21] L. Peide and X. Hongxue. Integrated one-stage model considering undesirable outputs for slacks-based measure of efficiency and super efficiency in data envelopment analysis. Journal of the Operational Research Society, 0(0):1–13, 2022.
[22] L. Peide, Z. Yizhen, and X. Hongxue. A neutral cross-efficiency measurement for general parallel production system. Expert Systems with Applications, 205:117778, 2022.
[23] M. C. A. S. Portela, E. Thanassoulis, and G. G. Simpson. A negative data in dea: a directional distance approach applied to bank branches. Journal of the Operational Research Society, 55(10):1111–1121, 2004.
[24] H. Scheel. Undesirable outputs in efficiency valuations. Eur. J. Oper. Res., 132:400–410, 2001.
[25] L. M. Seiford and J. Zhu. Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142:16–20, 2002.
[26] T. Shahsavan, M. Sanei, G. Tohidi, F. H. Lotfi, and S. Ghobadi. A new method of determining decision-making unit congestion under inter-temporal dependence. Soft Comput, 26:2063–2073, 2022.
[27] M. Soltanifar, M. Ghiyasi, and H. Sharafi. Inverse DEA-R models for merger analysis with negative data. IMA Journal of Management Mathematics, (In print):10.1093/imaman/dpac001, 2022.
[28] M. Soltanifar and H. Sharafi. A modified dea cross efficiency method with negative data and its application in supplier selection. Journal of Combinatorial Optimization, 43:265–296, 2022.
[29] Q. L. Wei, J. Z. Zhang, and X. S. Zhang. An inverse dea model for inputs/outputs estimate. European Journal of Operational Research, 121(1):151–163, 2000.
[30] E. Zeinodin and S. Ghobadi. Merging dmus based on of the idea inverse dea. Iranian Journal of Optimization, 11(2):77–84, 2018.
[31] E. Zenodin and S. Ghobadi. Merging decision-making units under inter-temporal dependence. IMA Journal of Management Mathematics, 31(2):139–16, 2020.
[32] X. Zhang and J. Cui. a project evaluation system in the state economic information system of china: An operation research practice in public sectore. International Transactions in Operational, 6:441–452, 1999.