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Efficiency Evaluation and Ranking DMUs in the Presence of Interval Data with Stochastic Bounds
Int. J. Data Envelopment Analysis
unpublished
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On account of the existence of uncertainty, DEA occasionally faces the situation of imprecise data, especially when a set of DMUs include missing data, ordinal data, interval data, stochastic data, or fuzzy data. Therefore, how to evaluate the efficiency of a set of DMUs in interval environments is a problem worth studying. In this paper, we discussed the new method for evaluation and ranking interval data with stochastic bounds. The approach is exemplified by numerical examples.
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