Titlebook: Data Envelopment Analysis; A Handbook of Models Joe Zhu Book 2015 Springer Science+Business Media New York 2015 DEA.DEA Models.Data Envelop

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Congestion

甜食

Immortal

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Factors Associated with Variationed that all DMUs under consideration be evaluated against each other in a same pool. Adding or deleting an inefficient DMU does not alter the efficient frontier and the efficiencies of the existing DMUs. The inefficiency scores change only if the efficient frontier is altered. Benchmarking is the pr

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Renal handling of drugs and xenobioticsata is missing and has to be estimated, or the situation has not occurred yet and the data has to be predicted. There are also cases where the factors are qualitative, and thus the data cannot be measured precisely. In these cases, fuzzy numbers can be used to represent the imprecise values, and thi

ARBOR

Andrew Whelton,Til Sturmer,George A. Portertput setting. While it is generally assumed that all outputs are impacted by all inputs, there are many situations where this may not be the case. For example, in a food manufacturing setting, certain foods are exempt from nutrition labeling and as a result are not influenced by labeling resources.

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Hans J. ten Donkelaar,Akira Horiis to eliminate the DMU under evaluation from the reference set. When applied to the variable returns to scale (VRS) situation, the resulting super-efficiency model may become infeasible for certain DMUs due to the convexity constraint. Infeasibility restricts a wider use of super-efficiency DEA. Th

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Hans J. ten Donkelaar,Akira Horiaddressed, e.g., via data transformation. These studies were scattered in the literature and often confined to some particular applications. In this paper, we present a systematic investigation concerning the building of DEA models. First, we describe the desirability of inputs and outputs, as well