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 32 publications

(13 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 4 more Smart Citations

Measuring congestion in data envelopment analysis without solving any models

2019

View full text Add to dashboard Cite

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.

show abstract

“…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

View full text Add to dashboard Cite

show abstract

Identification of Congestion in DEA

Mehdiloozad

Sahoo

Zhu

2020

International Series in Operations Research &Amp; Management Science

View full text Add to dashboard Cite

A new method of determining decision-making unit congestion under inter-temporal dependence

et al. 2022

View full text Add to dashboard Cite

Congestion is an important concept in data envelopment analysis (DEA). It occurs when the decrease of at least one decision making unit (DMU) input results in the increase of at least one output ineffective on other inputs or outputs. Until present, various methods have been discussed in terms of congestion determination in static DEA although no study has still been performed on congestion calculation in dynamic DEA under inter-temporal dependence. In this paper, congested input levels in multi-period production systems under inter-temporal dependence and further, the inter-temporal dependence of production periods due to the stock capital are studied. The advantage of dynamic DEA congestion method specification lies in the prevention of inappropriate resources allocation during evaluation periods. The validity of the proposed method has further been demonstrated via empirical example for better observation.

show abstract

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

Cited by 32 publications

References 21 publications

Measuring congestion in data envelopment analysis without solving any models

Measuring congestion in data envelopment analysis without solving any models

Identification of Congestion in DEA

A new method of determining decision-making unit congestion under inter-temporal dependence

Contact Info

Product

Resources

About