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2006

Back-propagation DEA

11 years 1 months ago
Back-propagation DEA
Data Envelopment Analysis (DEA) is one of the most widely used methods in the measurement efficiency and productivity of Decision Making Units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper introduces a neural network backpropagation Data Envelopment Analysis. Neural network requirements of computer memory and CPU time are far less than what is needed by conventional methods DEA and can be a useful tool in measuring efficiency of large datasets. Finally, the back-propagation DEA algorithm is applied to a large dataset to identify the source of inefficiency of DMUs and compare it with the result obtained by conventional DEA.
Ali Emrouznejad
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where DMIN
Authors Ali Emrouznejad
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