Back-propagation DEA
نویسنده
چکیده
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.
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