Negative data in DEA: Recognizing congestion and specifying the least and the most congested decision making units

Khoveyni, Mohammad; Eslami, Robabeh; Yang, Guo-liang

doi:10.1016/j.cor.2016.09.002

Cited by 33 publications

(13 citation statements)

References 21 publications

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“…Khoveyni et al [17] solved this example by their models. Here we apply our method to this example and compare the results in Table 3.…”

Section: Examplementioning

confidence: 98%

“…Khoveyni et al [17] represented their method for negative data base on the slack-based DEA approach. Assume that ,…”

Section:    mentioning

confidence: 99%

“…If the model (5) was feasible, then has congestion. For determining the type of its congestion (strong or weak), Khoveyni et al [17] represented the following model: are the obtained optimal solutions from model (5) that are fixed in model (6).…”

Section:    mentioning

confidence: 99%

“…If the model (5) was infeasible, then doesn't have congestion. Then Khoveyni et al [17] represented the following model:…”

Section:    mentioning

confidence: 99%

“…Ebrahimzade Adimi et al [16] represent a method to find the congestion hyperplane. Khoveyni et al [17] represent their method for identifying congestion with negative data. Wang et al [18] present a new definition of congestion.…”

Section: Introductionmentioning

confidence: 99%

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Measuring congestion in data envelopment analysis without solving any models

2019

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One of the important topics in Data Envelopment Analysis is congestion. Many scholars research in this field and represent their methods. In most of the represented methods, we have to solve lots of models or its used for a special aim like negative data, integer data, different Production Possibility Set and etc. Here we represent our method that measures the congestion without solving a model. It can be used for different Production Possibility Set (different technology) like and ; different data like negative data and integer data. Also, we can distinguish strongly or weakly congestion of Decision Making Unit. Furthermore, each DMU has congestion, efficient and inefficient, we can measure it by this method. Finally, we represent some numerical example of our purpose method and compare our method with other methods then show the results in tables.

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“…Khoveyni et al [17] solved this example by their models. Here we apply our method to this example and compare the results in Table 3.…”

Section: Examplementioning

confidence: 98%

“…Khoveyni et al [17] represented their method for negative data base on the slack-based DEA approach. Assume that ,…”

Section:    mentioning

confidence: 99%

Section:    mentioning

confidence: 99%

“…If the model (5) was infeasible, then doesn't have congestion. Then Khoveyni et al [17] represented the following model:…”

Section:    mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Measuring congestion in data envelopment analysis without solving any models

2019

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Efficiency Assessment of Mutual Fund With Risk and Negative Data: An Improved Two‐Stage Network SBM Model

Shi,

Pei,

Liu

2024

Manage Decis Econ

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This paper innovatively proposes an improved two‐stage network SBM model, which could handle negative data and incorporates the CVaR indicator representing risks in efficiency evaluation. The proposed approach is utilized to evaluate and analyze the overall efficiency and two subsystems' efficiencies of 29 mutual funds in China from 2018 to 2022. Empirical results demonstrate that both operational management efficiency and portfolio management efficiency significantly impact overall efficiency. Funds exhibiting excellent performance in operational management subsystem are more likely to achieve outstanding overall performance. Based on the empirical results, some policy recommendations are proposed.

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