Merging decision-making units in the simultaneous presence of desirable and undesirable factors

IFORS 2023

Open Access

Issue		RAIRO-Oper. Res. Volume 58, Number 2, March-April 2024 IFORS 2023


Page(s)		1529 - 1554
DOI		https://doi.org/10.1051/ro/2024034
Published online		05 April 2024

A. Ala, A. Goli, S. Mirjalili and V. Simic, A fuzzy multi-objective optimization model for sustainable healthcare supply chain network design. Appl. Soft Comput. 150 (2024) 111012. [CrossRef] [Google Scholar]
G.R. Amin and S. Al-Muharrami, A new inverse data envelopment analysis model for mergers with negative data. IMA J. Manag. Math. (2016) 1–13. [Google Scholar]
G.R. Amin, A. Emrouznejad and S. Gattoufi, Minor and major consolidations in inverse dea: Definition and determination. Comput. Ind. Eng. 103 (2017) 193–200. [CrossRef] [Google Scholar]
A. Amirteimoori, S. Kordrostami and M. Sarparast, Modeling undesirable factors in data envelopment analysis. Appl. Math. Comput. 180 (2006) 444–452. [MathSciNet] [Google Scholar]
T.-S. Chang, J.-G. Lin and J. Ouenniche, Dea-based nash bargaining approach to merger target selection. Eur. J. Oper. Res. 305 (2023) 930–945. [CrossRef] [Google Scholar]
A. Charnes, W.W. Cooper and E. Rhodes, Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2 (1978) 429–444. [Google Scholar]
L. Chen, Y. Wang, F. Lai and F. Feng, An investment analysis for china’s sustainable development based on inverse data envelopment analysis. J. Clean. Prod. 142 (2017) 1638–1649. [CrossRef] [Google Scholar]
Y.H. Chung, R. F¨are and S. Grosskopf, Productivity and undesirable outputs: a directional distance function approach. J. Environ. Manag. 51 (1997) 229–240. [CrossRef] [Google Scholar]
Z.D. Daryani, G. Tohidi, B. Daneshian, S. Razavyan and L.F. Hosseinzadeh, Inverse dea in two-stage systems based on allocative efficiency. J. Intell. Fuzzy Syst. 40 (2021) 591–603. [CrossRef] [Google Scholar]
L. Dong Joon, Inverse dea with frontier changes for new target setting. Eur. J. Oper. Res. 254 (2016) 510–516. [CrossRef] [Google Scholar]
M. Ehrgott, Multicriteria Optimization. Springer, Berlin (2005). [Google Scholar]
A. Emrouznejad and E. Thanassoulis, A mathematical model for dynamic efficiency using data envelopment analysis. Appl. Math. Comput. 160 (2005) 363–378. [Google Scholar]
A. Emrouznejad, G.-L. Yang and G.R. Amin, A novel inverse dea model with application to allocate the CO₂ emissions quota to different regions in chinese manufacturing industries. J. Oper. Res. Soc. (2018) 1–12. [Google Scholar]
M. Eyni, G. Tohidi and S. Mehrabeian, Applying inverse dea and cone constraint to sensitivity analysis of dmus with undesirable inputs and outputs. J. Oper. Res. Soc. 68 (2017) 34–40. [CrossRef] [Google Scholar]
R. Fare and S. Grosskopf, Modeling undesirable factors in efficiency evaluation: Comment. Eur. J. Oper. Res. 157 (2004) 242–245. [CrossRef] [Google Scholar]
R. Fare, S. Grosskopf, C. Lovell and C. Pasurka, Multilateral productivity comparisons when some outputs are undesirable: A nonparametric approach. Rev. Econ. Stat. 71 (1989) 90–98. [CrossRef] [Google Scholar]
S. Gattoufi, G.R. Amin and A. Emrouznejad, A new inverse dea method for merging banks. IMA J. Manag. Math. 25 (2014) 73–87. [Google Scholar]
M. Ghiyasi, Inverse dea based on cost and revenue efficiency. Comput. Ind. Eng. 114 (2017) 258–263. [CrossRef] [Google Scholar]
M. Ghiyasi, Efficiency improvement and resource estimation: A tradeoff analysis. Int. J. Product. Qual. Manag. 25 (2018) 151–169. [CrossRef] [Google Scholar]
M. Ghiyasi, Novel criterion models in the inverse dea problem. Int. J. Oper. Res. 35 (2019) 20–36. [CrossRef] [MathSciNet] [Google Scholar]
S. Ghobadi, Inverse dea using enhanced russell measure in the presence of fuzzy data. Int. J. Ind. Math. 10 (2018) 1–16. [Google Scholar]
S. Ghobadi, A dynamic dea model for resource allocation. Int. J. Math. Oper. Res. 17 (2020) 50–77. [CrossRef] [MathSciNet] [Google Scholar]
S. Ghobadi, Merging decision-making units with interval data. RAIRO:RO 55 (2021) 1605–1630. [Google Scholar]
S. Ghobadi and S. Jahangiri, Optimal allocation of resources using the ideal-solutions. J. New Res. Math. 5 (2019) 121–134. [Google Scholar]
S. Ghobadi and K. Soleimani-Chamkhorami, Merging decision-making units with fuzzy data. Asia-Pac. J. Oper. Res. (2021) 2140012. [Google Scholar]
F. Guijarro, M. Martinez-Gomez and D. Visbal-Cadavid, A model for sector restructuring through genetic algorithm and inverse dea. Expert Syst. Appl. 154 (2020) 113422. [CrossRef] [Google Scholar]
A. Hadi-vencheh, A. Hatami-marbini and Z.K.G. Ghelej Beigi, An inverse optimization model for imprecise data envelopment analysis. Opimization 64 (2015) 2441–2452. [CrossRef] [Google Scholar]
G. Halkos and K.N. Petrou, Treating undesirable outputs in dea: A critical review. Econ. Anal. Policy 62 (2019) 97–104. [CrossRef] [Google Scholar]
X. Hu, J. Li, X. Li and J. Cui, A revised inverse data envelopment analysis model based on radial models. Mathematics 8 (2020) 1–17. [Google Scholar]
G.R. Jahanshahloo, M. Soleimani-damaneh and S. Ghobadi, Inverse dea under inter-temporal dependence using multiple-objective programming. Eur. J. Oper. Res. 240 (2015) 447–456. [CrossRef] [Google Scholar]
H. Kader and J. Spaak, Merger and acquisition: The impact on organizational culture, creativity and product innovation - A case study, Bachelor thesis, Department of Business Studies, Uppsala University (2014). [Google Scholar]
C. Kao and S.-N. Hwang, Measuring the effects of undesirable outputs on the efficiency of production units. Eur. J. Oper. Res. 292 (2021) 996–1003. [CrossRef] [Google Scholar]
S. Kordrostami and M.J.S. Noveiri, The overall efficiency of decision making units with undesirable outputs. 46th Annual Iranian Mathematics Conference, 25–28 August. Yazd University, Iran (2015) 1–4. [Google Scholar]
W. Liu and J. Sharp, Dea models via goal programming, in data envelopment analysis in the service sector, edited by G. Westermann, Deutscher Universitatsverlag, Wiesbaden, Germany (1999) 97–101. [Google Scholar]
P. Liu and H. Xu, Integrated one-stage models considering undesirable outputs and weighting preference in slacks-based measure of efficiency and superefficiency. J. Oper. Res. Soc. (2022) 1–13. [Google Scholar]
W.B. Liu, W. Meng, X.X. Li and D.Q. Zhang, Dea models with undesirable inputs and outputs. Ann. Oper. Res. 173 (2010) 177–194. [CrossRef] [MathSciNet] [Google Scholar]
R. Lotfi, B. Kargar, A. Gharehbaghi, M. Afshar, M.S. Rajabi and N. Mardani, A data-driven robust optimization for multi-objective renewable energy location by considering risk. Environ. Dev. Sustain. (2022) 1–22. [Google Scholar]
R. Lotfi, A. Gharehbaghi, M.S. Mehrjardi, K. Kheiri and S.S. Ali, A robust, resilience multi-criteria decision-making with risk approach: A case study for renewable energy location. Environ. Sci. Pollut. Res. 30 (2023) 43267–43278. [CrossRef] [Google Scholar]
M. Majid Kalantary and R. Farzipoor Saen, Assessing sustainability of supply chains: An inverse network dynamic dea model. Comput. Ind. Eng. 135 (2019) 1224–1238. [CrossRef] [Google Scholar]
H. Moazeni, B.A. Shirani and S.R. Hejazi, An integrated approach for the merger of small and medium-sized industrial units. RAIRO:RO 57 (2023) 939–965. [CrossRef] [EDP Sciences] [Google Scholar]
H. Omrani, M. Shamsi and A. Emrouznejad, Evaluating sustainable efficiency of decision-making units considering undesirable outputs: An application to airline using integrated multi-objective dea-topsis. Environ. Dev. Sustain. 25 (2023) 5899–5930. [CrossRef] [PubMed] [Google Scholar]
A. Oukil, R.E. Kennedy, A. Al-Hajri and A.A. Soltani, Unveiling the potential of hotel mergers: A hybrid dea approach for optimizing sector-wide performance in the hospitality industry. Int. J. Hosp. Manag. 116 (2024) 103620. [CrossRef] [Google Scholar]
F.S. Parashkouh, S. Kordrostami, A. Amirteimoori and A. Ghane-Kanafi, Modelling undesirable products in non-parametric performance analysis. J. Model. Manag. 16 (2021) 267–287. [CrossRef] [Google Scholar]
J.T. Pastor, J.L. Ruiz and I. Sirvent, An enhanced dea russell graph efficiency measure. Eur. J. Oper. Res. 115 (1999) 596–607. [CrossRef] [Google Scholar]
N.A. Ramli, S. Munisamy and B. Arabi, Scale directional distance function and its application to the measurement of eco-efficiency in the manufacturing sector. Ann. Oper. Res. 211 (2013) 381–398. [CrossRef] [Google Scholar]
L.M. Seiford and J. Zhu, Modeling undesirable factors in efficiency evaluation. Eur. J. Oper. Res. 142 (2002) 16–20. [Google Scholar]
X. Shi, A. Emrouznejad and W. Yu, Overall efficiency of operational process with undesirable outputs containing both series and parallel processes: A sbm network dea model. Expert Syst. Appl. 178 (2021) 115062. [CrossRef] [Google Scholar]
T. Skevas, S.E. Stefanou and A.O. Lansink, Pesticide use, environmental spillovers and efficiency: A dea risk-adjusted efficiency approach applied to dutch arable farming. Eur. J. Oper. Res. 237 (2014) 658–664. [CrossRef] [Google Scholar]
K. Soleimani-Chamkhorami, F.H. Lotfi, G.R. Jahanshahloo and M. Rostamy-Malkhalifeh, Preserving cost and revenue efficiency through inverse data envelopment analysis models. INFOR: Inf. Syst. Oper. Res. 58 (2020) 561–578. [Google Scholar]
K. Soleimani-Chamkhorami, F.H. Lotfi, G.R. Jahanshahloo and M. Rostamy-Malkhalifeh, A ranking system based on inverse data envelopment analysis. IMA J. Manag. Math. 31 (2020) 367–385. [MathSciNet] [Google Scholar]
F.L. Takahashi and M.R. Vasconcelos, Bank efficiency and undesirable output: An analysis of non-performing loans in the brazilian banking sector. Finance Res. Lett. 59 (2024) 104651. [CrossRef] [Google Scholar]
F. Taher, B. Daneshian, G. Tohidi, F. Hosseinzadeh lotfi and F.M. Khiyabani, Estimation of undesirable outputs and desirable inputs along with efficiency improving for dmus with interval data. J. New Res. Math. 7 (2021) 65–72. [Google Scholar]
M. Toloo and J. Hanclova, Multi-valued measures in dea in the presence of undesirable outputs. Omega 94 (2020) 102041. [CrossRef] [Google Scholar]
D. Tyteca, Linear programming models for the measurment of enviromental performance of firms concepts and empirical results. J. Product. Anal. 8 (1997) 183–197. [CrossRef] [Google Scholar]
A.H. Vencheh, R. Kazemi Matin and M. Tavassoli Kajani, Undesirable factors in efficiency measurement. Appl. Math. Comput. 163 (2005) 547–552. [MathSciNet] [Google Scholar]
M. Wegener and G.R. Amin, Minimizing greenhouse gas emissions using inverse dea with an application in oil and gas. Expert Syst. Appl. 122 (2019) 369–375. [CrossRef] [Google Scholar]
V. Wojcik, H. Dyckhoff and S. Gutgesell, The desirable input of undesirable factors in data envelopment analysis. Ann. Oper. Res. 259 (2017) 461–484. [CrossRef] [MathSciNet] [Google Scholar]
S. Xiao, Y. Li, A. Emrouznejad, J. Xie and L. Liang, Estimation of potential gains from bank mergers: A novel two-stage cost efficiency dea model. J. Oper. Res. Soc. 68 (2017) 1045–1055. [CrossRef] [Google Scholar]
E. Zenodin and S. Ghobadi, Merging decision-making units under inter-temporal dependence. IMA J. Manag. Math. 31 (2020) 139–16. [MathSciNet] [Google Scholar]
X. Zhang and J. Cui, a project evaluation system in the state economic information system of china: An operation research practice in public sectore. Int. Trans. Oper. 6 (1999) 441–452. [Google Scholar]
P. Zhou, K. Leng Poh and B. Wah Ang, A non-radial dea approach to measuring environmental performance. Eur. J. Oper. Res. 178 (2007) 1–9. [CrossRef] [Google Scholar]
Z. Zhou, G. Xu, C. Wang and J. Wu, Modeling undesirable output with a dea approach based on an exponential transformation: An application to measure the energy efficiency of chinese industry. J. Clean. Prod. 236 (2019) 117717. [CrossRef] [Google Scholar]

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