Merging decision-making units with interval data

Ghobadi, Saeid

doi:10.1051/ro/2020029

Cited by 19 publications

(4 citation statements)

References 44 publications

(67 reference statements)

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“…According to the pessimistic and optimistic viewpoints (Despotis and Smirlis (2002); Entani et al (2002); Ghobadi (2021)), the following models proposed to estimate the upper and lower bounds of efficiency of DM U n+1 with (1 − α)% confidence interval:…”

Section: Estimation Of Output Levelsmentioning

confidence: 99%

Inverse data envelopment analysis with stochastic data

et al. 2021

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The inverse Data Envelopment Analysis (InvDEA) is an exciting and significant topic in the DEA area. Also, uncertain data in various real-life applications can degrade the efficiency results. The current work addresses the InvDEA in the presence of stochastic data. Under maintaining the efficiency score, the inputs/outputs-estimation problem is investigated when some or all of its outputs/inputs increase. A novel optimality concept for multiple-objective programming problems, stochastic (weak) Pareto optimality in the level of significance [[EQUATION]] , is introduced to derive necessary and sufficient conditions for input/output estimation. Furthermore, the performance of the developed theory in a banking sector application is verified.

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Section: Estimation Of Output Levelsmentioning

confidence: 99%

Inverse data envelopment analysis with stochastic data

et al. 2021

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“…The inverse DEA idea has been initially proposed by Zhang and Cui (1999). This technique can be utilized in the various theoretical and practical frameworks of DEA such as; resource allocation (Wei et al (2000); Hadi-Vencheh et al (2008); Lertworasirikul et al (2011)); sensitivity analysis (Dong Joon (2016); Amin et al (2017a)); investment analysis (Lin (2010)); under inter-temporal dependence of data (Jahanshahloo et al (2015); Ghobadi (2019)); ratio data (Sohrabi et al ( 2022)); firm's merging (Gattoufi et al (2014); Amin and Al-Muharrami (2016); Zenodin and Ghobadi (2020); Ghobadi (2021); Amin and Ibn Boamah (2021);Ghobadi and Soleimani-Chamkhorami (2021);Soltanifar et al (2022)) and firm's re-structuring (Amin et al (2017b);Gerami et al (2021b)). In this research, we develop traditional InvDEA models by integrating InvDEA and Ratio Analysis so that the power of the analysis's discrimination increases.…”

Section: Introductionmentioning

confidence: 99%

Inverse DEA-R models for generalized restructuring analysis

Ghobadi

Zanboori

Soleimani-Chamkhorami

2022

Preprint

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A generalized restructuring refers to an action in a commercial environment in which a homogeneous set of units is restructured to create a new set of units in the same environment to achieve specific performance goals. Inverse data envelopment analysis (InvDEA) is a useful analytical tool based on multiple-objective mathematical programming to estimate the required levels of input/output to achieve given efficiency targets. This paper is devoted to develop a new InvDEA based methodology with the data in ratio form, called InvDEA-R, for modeling the generalized restructuring. The most important advantages of the proposed models are: i) Maintaining the confidentiality of data, which can strengthen the motivation of managers to participate in the process of improving efficiency through reconstruction; ii) In addition to using pure input and output data, it considers a combination of their ratios in the reconstruction process. This not only increases the more appropriate reconstruction analysis’s discrimination power, but also provides a reasonable endogenous weight restriction framework for using the expert view in the generalized units restructuring process. This paper also presents a DEA-R estimator model to determine the minimum efficiency aims that post-restructuring units can gain. The developed restructure theory is examined via a banking sector application.

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“…In the meantime, the issue of merging decision-making units was raised, and many articles are provided, Amin et al (2017), , and Lin et al (2020). Ghobadi (2021), mentioned that for the aim of merging units with interval data when having the imprecise or ambiguous data, the inverse DEA concept and linear programming models could be employed to estimate the inherited inputs/outputs of the merged units, and identify the least and most achievable efficiency targets from the merged unit. Zeinodin and Ghobadi (2020), provided researches that deals with merging of decision-making units under the inter-temporal dependence assumption.…”

Section: Introductionmentioning

confidence: 99%