Paper Info
Title
Improved DEA models in the presence of undesirable outputs and imprecise data: an application to banking industry in India
Abstract
Data envelopment analysis (DEA) is a widely used non-parametric technique for measuring the performance of a homogeneous set of decision making units (DMUs). The basic DEA models are typically based on the conjecture of input minimization and output maximization, and are limited to crisp data. Therefore, the present paper focuses on the DEA models that can handle undesirable outputs and imprecise input–output data forms like intervals or ordinal relations or fuzzy numbers. These models measure the final efficiency of each DMU as an interval for interval and ordinal data, and a fuzzy number for fuzzy data. Moreover, comparison with the existing models show that the proposed models are more theoretically accurate, numerically efficient and measure less number of DMUs as efficient. In addition, some numerical examples with different data sets and an application to the banking industry in India are presented to validate effectiveness of the proposed models.
Year
DOI
Venue
2017
10.1007/s13198-017-0634-4
International Journal of Systems Assurance Engineering and Management
Keywords
DocType
Volume
Data envelopment analysis, Undesirable outputs, Imprecise data, Interval efficiency, Fuzzy efficiency
Journal
8
Issue
ISSN
Citations 
2
0976-4348
0
PageRank 
References 
Authors
0.34
19
3
Name
Order
Citations
PageRank
Jolly Puri1393.66
Jolly Puri2393.66
Shiv Prasad Yadav311715.90