Abstract
In many data envelopment analysis (DEA) applications, the analyst always confronts the difficulty that the selected data set is not suitable to apply traditional DEA models for their poor discrimination. This paper presents an approach using Shannon’s entropy to improve the discrimination of traditional DEA models. In this approach, DEA efficiencies are first calculated for all possible variable subsets and analyzed using Shannon’s entropy theory to calculate the degree of the importance of each subset in the performance measurement, then we combine the obtained efficiencies and the degrees of importance to generate a comprehensive efficiency score (CES), which can observably improve the discrimination of traditional DEA models. Finally, the proposed approach has been applied to some data sets from the prior DEA literature.
Highlights
Data envelopment analysis (DEA) has been proven to be an effective tool for performance evaluation and benchmarking since it was first introduced in [1]
This paper presents an approach to improve the discrimination of traditional DEA methods without losing variable information
When these DEA models are extended from the constant return to scale (CRS) version to variable return to scale (VRS) version, the game cross DEA model may be problematic for the existence of the negative efficiency scores [32] and super-efficiency DEA model may be infeasible [35,36]
Summary
Data envelopment analysis (DEA) has been proven to be an effective tool for performance evaluation and benchmarking since it was first introduced in [1]. Adding variables to a DEA model will result in higher dimensionality of the weight space and higher efficiency scores, as well as an expanded set of efficient DMUs [6]. Reference [14] measured the ecology efficiencies of 17 Chinese cities with 30 variables These aforementioned data sets have a common characteristic which is that the number of variables is too large to directly apply DEA methods to the criteria of the number of variables being “large” is according to the guideline [8]. It is wise to try different models and combine the results of these different models [17] To this end, this paper presents an approach to improve the discrimination of traditional DEA methods without losing variable information.
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