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.
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Official URL: http://dx.doi.org/10.1109/IEEM.2011.6117897
Abstract
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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | data envelopment analysis |
| Divisions: | Mechanical Engineering |
| ID Code: | 45509 |
| Deposited By: | Haliza Zainal |
| Deposited On: | 10 Jun 2015 03:00 |
| Last Modified: | 20 Sep 2017 01:36 |
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