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Title of Journal: Neural Comput Applic

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Abbravation: Neural Computing and Applications

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Springer London

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DOI

10.1007/bf02590273

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ISSN

1433-3058

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Efficiency measurement in data envelopment analysi

Authors: Bohlool Ebrahimi Madjid Tavana Morteza Rahmani Francisco J SantosArteaga
Publish Date: 2016/12/27
Volume: 30, Issue: 6, Pages: 1971-1982
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Abstract

Several methods have been proposed in data envelopment analysis DEA for measuring efficiency in problems with interval or ordinal data In this study we review the weaknesses and drawbacks of these methods and show how converting ordinal or interval data into precise data can lead to violations of established DEA axioms One of the axioms violated by these conversion processes is the inclusion of observations axiom which requires a consistent definition of the production possibility set We describe the special properties of ordinal and interval data together with their effect on the DEAbased rankings using a theorem and an example We also propose a new algorithm and apply random dataset generation to overcome the problems arising from violations of the inclusion of observations axiom in DEA settings with ordinal or internal data Several numerical examples are presented to demonstrate the applicability and exhibit the efficacy of the proposed method

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