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.

• Type: Conference
• Year: 2006
• Where: DMIN
• Authors: Ali Emrouznejad
• Keywords: CPU Time, Data Envelopment Analysis, DMIN 2006, DMIN 2007, Large Dataset