Paper Info
Title
A full ranking methodology in data envelopment analysis based on a set of dummy decision making units.
Abstract
We propose a full ranking methodology in DEA based on five dummy DMUs.The core of our approach is a new linear program.We produce common weights for full ranking of the DMUsThree literature examples have been tested and examined.The new method produces a rationale ranking compared with literature methods. In this paper, we propose a new methodology for ranking decision making units in data envelopment analysis (DEA). Our approach is a benchmarking method, seeks a common set of weights using a proposed linear programming model and is based on the TOPSIS approach in multiple attribute decision making (MADM). To this end, five artificial or dummy decision making units (DMUs) are defined, the ideal DMU (IDMU), the anti-ideal DMU (ADMU), the right ideal DMU (RIDMU), the left anti-ideal DMU (LADMU) and the average DMU (AVDMU). We form two comprehensive indexes for the AVDMU called the Left Relative Closeness (LRC) and the Right Relative Closeness (RRC) with respect to the RIDMU and LADMU. The LRC and RRC indexes will be used in the new proposed linear programming model to estimate the common set of weights, the new efficiency of DMUs and finally an overall ranking for all the DMUs. The change of the ratio between LRC and RRC indexes is capable to be provoked alternative rankings. One of the best advantages of this model is that we can make a rationale ranking which is demonstrated by the realized correlation analysis. Also, the new proposed efficiency score of the DMUs is close to the efficiency score of the DEA (CCR) methodology. Three numerical examples are provided to illustrate the applicability of the new approach and the effectiveness of the new approach in DEA ranking in comparison with other conventional ranking methods. Also, an \"error\" analysis proves the robustness of the proposed methodology.
Year
DOI
Venue
2017
10.1016/j.eswa.2017.01.042
Expert Syst. Appl.
Keywords
Field
DocType
Data envelopment analysis,Performance evaluation,Linear programming,Heuristics,Ranking,Relative closeness (RC)
Data mining,Mathematical optimization,Ranking SVM,Ranking,Computer science,Robustness (computer science),Heuristics,Linear programming,Data envelopment analysis,TOPSIS,Benchmarking
Journal
Volume
Issue
ISSN
77
C
0957-4174
Citations 
PageRank 
References 
5
0.42
23
Authors
1
Name
Order
Citations
PageRank
Manolis N. Kritikos192.59