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.447022017-08-30T03:04:38Z http://eprints.utm.my/id/eprint/44702/ A review of data envelopment analysis models for handling data variations Chuen, Tse Kuah Kuan, Yew Wong HA Statistics 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. Institute of Electrical and Electronics Engineers 2011 Article PeerReviewed Chuen, Tse Kuah and Kuan, Yew Wong (2011) A review of data envelopment analysis models for handling data variations. IEEE International Conference on Industrial Engineering and Engineering Management . pp. 151-155. ISSN 2157-3611 |
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HA Statistics Chuen, Tse Kuah Kuan, Yew Wong A review of data envelopment analysis models for handling data variations |
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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 |
Article |
| author |
Chuen, Tse Kuah Kuan, Yew Wong |
| author_facet |
Chuen, Tse Kuah Kuan, Yew Wong |
| 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 |
| publisher |
Institute of Electrical and Electronics Engineers |
| publishDate |
2011 |
| url |
http://eprints.utm.my/id/eprint/44702/ |
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1643651518332665856 |