Abstract
This paper presents a common weight multi-criteria decision making (MCDM) approach for determining the best decision making unit (DMU) taking into consideration multiple inputs and outputs. Its robustness and discriminating power are illustrated through comparing the results with those obtained by data envelopment analysis (DEA) and its extensions such as cross efficiency analysis and minimax efficiency DEA model, which yield a ranking with an improved discriminating power. Several examples reported in earlier research addressing DEA’s discriminating power are used to illustrate the application of the proposed approach. The results indicate that the proposed framework enables further ranking of DEA-efficient DMUs with a notable saving in the number of mathematical programming models solved.
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© 2007 Springer-Verlag Berlin Heidelberg
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Karsak, E.E., Ahiska, S.S. (2007). A Common-Weight MCDM Framework for Decision Problems with Multiple Inputs and Outputs. In: Gervasi, O., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2007. ICCSA 2007. Lecture Notes in Computer Science, vol 4705. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74472-6_64
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DOI: https://doi.org/10.1007/978-3-540-74472-6_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74468-9
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