A review of data envelopment analysis models for handling data variations
Conventional data envelopment analysis (DEA) models require that the inputs and outputs to be measured deterministically. However, in real world applications, the measurements are subjected to random noise and errors. Ignoring the randomness in the measurement would render an evaluation using DEA un...
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2011
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my.utm.455092017-09-20T01:36:18Z http://eprints.utm.my/id/eprint/45509/ A review of data envelopment analysis models for handling data variations Chuen, Tse Kuah Wong, Kuan Yew Conventional data envelopment analysis (DEA) models require that the inputs and outputs to be measured deterministically. However, in real world applications, the measurements are subjected to random noise and errors. Ignoring the randomness in the measurement would render an evaluation using DEA unreliable. In response to this particular weakness of DEA, a number of DEA models have been proposed in the literature. This paper's aim is to review the major DEA models for handling data variations. The models include Stochastic DEA (SDEA), Fuzzy DEA (FDEA), and Imprecise DEA (IDEA). Some future research directions in this area will be highlighted as well. 2011 Conference or Workshop Item PeerReviewed Chuen, Tse Kuah and Wong, Kuan Yew (2011) A review of data envelopment analysis models for handling data variations. In: The IEEE International Conference On Industrial Engineering And Engineering Management. http://dx.doi.org/10.1109/IEEM.2011.6117897 |
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Conventional data envelopment analysis (DEA) models require that the inputs and outputs to be measured deterministically. However, in real world applications, the measurements are subjected to random noise and errors. Ignoring the randomness in the measurement would render an evaluation using DEA unreliable. In response to this particular weakness of DEA, a number of DEA models have been proposed in the literature. This paper's aim is to review the major DEA models for handling data variations. The models include Stochastic DEA (SDEA), Fuzzy DEA (FDEA), and Imprecise DEA (IDEA). Some future research directions in this area will be highlighted as well. |
| format |
Conference or Workshop Item |
| author |
Chuen, Tse Kuah Wong, Kuan Yew |
| spellingShingle |
Chuen, Tse Kuah Wong, Kuan Yew A review of data envelopment analysis models for handling data variations |
| author_facet |
Chuen, Tse Kuah Wong, Kuan Yew |
| author_sort |
Chuen, Tse Kuah |
| title |
A review of data envelopment analysis models for handling data variations |
| title_short |
A review of data envelopment analysis models for handling data variations |
| title_full |
A review of data envelopment analysis models for handling data variations |
| title_fullStr |
A review of data envelopment analysis models for handling data variations |
| title_full_unstemmed |
A review of data envelopment analysis models for handling data variations |
| title_sort |
review of data envelopment analysis models for handling data variations |
| publishDate |
2011 |
| url |
http://eprints.utm.my/id/eprint/45509/ http://dx.doi.org/10.1109/IEEM.2011.6117897 |
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1643651761611735040 |