Commenced in January 2007
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Classifying and Predicting Efficiencies Using Interval DEA Grid Setting

Authors: Yiannis G. Smirlis

Abstract:

The classification and the prediction of efficiencies in Data Envelopment Analysis (DEA) is an important issue, especially in large scale problems or when new units frequently enter the under-assessment set. In this paper, we contribute to the subject by proposing a grid structure based on interval segmentations of the range of values for the inputs and outputs. Such intervals combined, define hyper-rectangles that partition the space of the problem. This structure, exploited by Interval DEA models and a dominance relation, acts as a DEA pre-processor, enabling the classification and prediction of efficiency scores, without applying any DEA models.

Keywords: Data envelopment analysis, interval DEA, efficiency classification, efficiency prediction.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1317188

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References:


[1] A. Charnes, W. W. Cooper and Rhodes E. “Measuring the efficiency of decision making units”, European Journal of Operational Research 1978; 2; 429-444.
[2] J. H. Dulá, F. J. López. “Data envelopment analysis (DEA) in massive data sets”, Kluwer Academic Publishers; 2002; ISBN 1-4020-0489-3.
[3] Y. Chen, A. I. Ali, “Output-Input ratio analysis and DEA frontier”, Journal of Operational Research, 2002, 142:476-479.
[4] Μ. Shaheen, “A Pre-Processor for the CCR Model in DEA”, in INFORMS National Conference, Miami, FL. (Nov. 6, 2001).
[5] Ali I. A, “Streamlined computation for data envelopment”, European Journal of Operational Research, Volume 64, Issue 1, 8 January 1993, Pages 61-67.
[6] E. W. Forgy (1965). "Cluster analysis of multivariate data: efficiency versus interpretability of classifications". Biometrics. 21: 768–769.
[7] W. W. Cooper, K. S. Park and G. Yu. “IDEA and AR-IDEA: Models for dealing with imprecise data in DEA”, Management Science. 1999; 45; 597-607.
[8] D. K. Despotis, Y. G. Smirlis, “Data Envelopment with Imprecise Data”, European Journal of Operational Research 2002; 140; 24-36.
[9] Wang. Y-M. R. Greatbanks, Jian-Bo Yang. 2005. “Interval efficiency assessment using data envelopment analysis”. Fuzzy Sets and Systems 153:347–370.