The Library
A comparison of data envelopment analysis and artificial neural networks as tools for assessing the efficiency of decision making units
Tools
UNSPECIFIED (1996) A comparison of data envelopment analysis and artificial neural networks as tools for assessing the efficiency of decision making units. JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 47 (8). pp. 1000-1016. ISSN 0160-5682.
Research output not available from this repository.
Request-a-Copy directly from author or use local Library Get it For Me service.
Abstract
This paper is concerned with the comparison of two popular non-parametric methodologies-data envelopment analysis and artificial neural networks-as tools for assessing performance. Data envelopment analysis has been established since 1978 as a superior alternative to traditional parametric methodologies, such as regression analysis, for assessing performance. Neural networks have recently been proposed as a method for assessing performance. In this paper, we use a simulated production technology of two inputs and one output for testing the success of the two methods for assessing efficiency. The two methods are also compared on their practical use as performance measurement tools on a set of bank branches, having multiple input and output criteria. The results demonstrate that, despite their differences, both methods offer a useful range of information regarding the assessment of performance.
Item Type: | Journal Article | ||||
---|---|---|---|---|---|
Subjects: | H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management | ||||
Journal or Publication Title: | JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY | ||||
Publisher: | STOCKTON PRESS | ||||
ISSN: | 0160-5682 | ||||
Official Date: | August 1996 | ||||
Dates: |
|
||||
Volume: | 47 | ||||
Number: | 8 | ||||
Number of Pages: | 17 | ||||
Page Range: | pp. 1000-1016 | ||||
Publication Status: | Published |
Data sourced from Thomson Reuters' Web of Knowledge
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |