Ranking efficient DMUs using minimizing distance in DEA

Ziari, Shokrollah; Raissi, Sadigh

doi:10.1007/s40092-016-0141-2

Cited by 12 publications

(6 citation statements)

References 22 publications

(22 reference statements)

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“…For example, Shetty and Pakkala [36] proposed the single virtual inefficient DMU used in DEA. Ziari and Raissi [37] improved the DEA model with the help of virtual DMUs. Since the number of energy storage technologies are limited in case study, the virtual DMUs can be used in the proposed model to enable it being applied in comparison of small amount of DMUs.…”

Section: Table 1 Literatures Related To Prioritization Of Energy Stor...mentioning

confidence: 99%

Comparative sustainability efficiency measurement of energy storages under uncertainty: An innovative framework based on interval SBM model

Lin

Man

Lee

et al. 2021

Journal of Energy Storage

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Section: Table 1 Literatures Related To Prioritization Of Energy Stor...mentioning

confidence: 99%

Comparative sustainability efficiency measurement of energy storages under uncertainty: An innovative framework based on interval SBM model

Lin

Man

Lee

et al. 2021

Journal of Energy Storage

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“…As a result, the number of efficient DMUs increases, however, some of which are not actually as efficient. When there is such a case, other post-optimality models may be applied to further rank DMUs (Ziari and Raissi 2016). These may include super-efficiency analysis (Anderson and Peterson 1993), Mehrabian-Alirezaee-Jahanshahloo (MAJ) model (Mehrabian et al 1999), a nonlinear programming L 1 -norm model (Jahanshahloo et al 2004), and leave-one-out idea and L 1 -norm (Rezai Balf et al 2012).…”

Section: Variable Selection In University Departmentsmentioning

confidence: 99%

Shapley value-based multi-objective data envelopment analysis application for assessing academic efficiency of university departments

et al. 2018

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This paper adopts a modified approach of data envelopment analysis (DEA) to measure the academic efficiency of university departments. In real-world case studies, conventional DEA models often identify too many decision-making units (DMUs) as efficient. This occurs when the number of DMUs under evaluation is not large enough compared to the total number of decision variables. To overcome this limitation and reduce the number of decision variables, multiobjective data envelopment analysis (MODEA) approach previously presented in the literature is applied. The MODEA approach applies Shapley value as a cooperative game to determine the appropriate weights and efficiency score of each category of inputs. To illustrate the performance of the adopted approach, a case study is conducted in a university in the Philippines. The input variables are academic staff, non-academic staff, classrooms, laboratories, research grants, and department expenditures, while the output variables are the number of graduates and publications. The results of the case study revealed that all DMUs are inefficient. DMUs with efficiency scores close to the ideal efficiency score may be emulated by other DMUs with least efficiency scores.

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“…Unlike previous models (that only presented models), it studies and measures the efficiency of the units. Ziari and Raissi (2016) developed a new model for ranking of strongly efficient DMUs in DEA by solving the infeasibility and unbounded difficulties of existing models. Esmaeilzadeh and Hadi-Vencheh (2015) proposed a procedure for complete ranking of DMUs in DEA.…”

Section: Literature Reviewmentioning

confidence: 99%

Practical benchmarking in DEA using artificial DMUs

2018

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Data envelopment analysis (DEA) is one of the most efficient tools for efficiency measurement which can be employed as a benchmarking method with multiple inputs and outputs. However, DEA does not provide any suggestions for improving efficient units, nor does it provide any benchmark or reference point for these efficient units. Impracticability of these benchmarks under environmental conditions is another challenge of benchmarking by DEA. The current study attempts to extend basic models for benchmarking of efficient units under practical conditions. To this end, we construct the practical production possibility set (PPPS) by employing the concept of artificial decision-making units and adding these decisionmaking units to the production possibility set (PPS) such that these artificial units satisfy all environmental constraints. Then, the theorems related to PPPS and their proofs are provided. Moreover, as a secondary result of this study, efficient units can be ranked according to their practical efficiency scores.

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Ranking efficient DMUs using minimizing distance in DEA

Cited by 12 publications

References 22 publications

Comparative sustainability efficiency measurement of energy storages under uncertainty: An innovative framework based on interval SBM model

Comparative sustainability efficiency measurement of energy storages under uncertainty: An innovative framework based on interval SBM model

Shapley value-based multi-objective data envelopment analysis application for assessing academic efficiency of university departments

Practical benchmarking in DEA using artificial DMUs

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