A combined neural network and DEA for measuring efficiency of large scale datasets
Emrouznejad, Ali and Shale, Estelle. (2009) A combined neural network and DEA for measuring efficiency of large scale datasets. Computers and Industrial Engineering, Vol.56 (No.1). pp. 249-254. ISSN 0360-8352Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/j.cie.2008.05.012
Data Envelopment Analysis (DEA) is one of the most widely used methods in the measurement of the 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 proposes a neural network back-propagation Data Envelopment Analysis to address this problem for the very large scale datasets now emerging in practice. Neural network requirements for computer memory and CPU time are far less than that needed by conventional DEA methods and can therefore be a useful tool in measuring the efficiency of large datasets. Finally, the back-propagation DEA algorithm is applied to five large datasets and compared with the results obtained by conventional DEA. (C) 2008 Elsevier Ltd. All rights reserved.
|Item Type:||Journal Article|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
|Divisions:||Faculty of Social Sciences > Warwick Business School|
|Journal or Publication Title:||Computers and Industrial Engineering|
|Publisher:||Pergamon-Elsevier Science Ltd.|
|Number of Pages:||6|
|Page Range:||pp. 249-254|
|Access rights to Published version:||Restricted or Subscription Access|
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