Paper Info
Title
Common weights for fully ranking decision making units by regression analysis
Abstract
Existing methods for generating common weights in data envelopment analysis (DEA) are either very complicated or unable to produce a full ranking for decision making units (DMUs). This paper proposes a new methodology based on regression analysis to seek a common set of weights that are easy to estimate and can produce a full ranking for DMUs. The DEA efficiencies obtained with the most favorable weights to each DMU are treated as the target efficiencies of DMUs and are best fitted with the efficiencies determined by common weights. Two new nonlinear regression models are constructed to optimally estimate the common weights. Four numerical examples are examined using the developed new models to test their discrimination power and illustrate their potential applications in fully ranking DMUs. Comparisons with a similar compromise approach for generating common weights are also discussed.
Year
DOI
Venue
2011
10.1016/j.eswa.2011.01.004
Expert Syst. Appl.
Keywords
Field
DocType
full ranking,new nonlinear regression model,data envelopment analysis,new model,common weight,common set,ranking dmus,ranking decision,new methodology,dea ranking,target efficiency,regression analysis,common weights,dea efficiency,nonlinear regression,optimal estimation,data envelope analysis
Data mining,Ranking,Computer science,Regression analysis,Nonlinear regression,Data envelopment analysis,Artificial intelligence,Statistics,Machine learning
Journal
Volume
Issue
ISSN
38
8
Expert Systems With Applications
Citations 
PageRank 
References 
17
0.64
6
Authors
3
Name
Order
Citations
PageRank
Ying-Ming Wang13256166.96
Ying Luo246720.48
Yi-Xin Lan3221.09