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 0360-8352

Full text not available from this repository.

Abstract

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 T Technology
Divisions:	Faculty of Social Sciences > Warwick Business School
Journal or Publication Title:	Computers and Industrial Engineering
Publisher:	Pergamon-Elsevier Science Ltd.
ISSN:	0360-8352
Official Date:	February 2009
Volume:	Vol.56
Number:	No.1
Number of Pages:	6
Page Range:	pp. 249-254
Identification Number:	10.1016/j.cie.2008.05.012
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
URI:	http://wrap.warwick.ac.uk/id/eprint/28611

Data sourced from Thomson Reuters' Web of Knowledge

Request changes or add full text files to a record

Actions (login required)

View Item