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Open AccessArticle

Determining Common Weights in Data Envelopment Analysis with Shannon’s Entropy

by Xiao-Guang Qi *,† and Bo Guo
College of Information System and Management, National University of Defense Technology, Changsha 410073, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Entropy 2014, 16(12), 6394-6414; https://doi.org/10.3390/e16126394
Received: 17 October 2014 / Revised: 29 November 2014 / Accepted: 2 December 2014 / Published: 4 December 2014
Data Envelopment Analysis (DEA) is a non-parametric method for evaluating the efficiency of Decision Making Units (DMUs) with multiple inputs and outputs. In the traditional DEA models, the DMU is allowed to use its most favorable multiplier weights to maximize its efficiency. There is usually more than one efficient DMU which cannot be further discriminated. Evaluating DMUs with different multiplier weights would also be somewhat irrational in practice. The common weights DEA model is an effective method for solving these problems. In this paper, we propose a methodology combining the common weights DEA with Shannon’s entropy. In our methodology, we propose a modified weight restricted DEA model for calculating non-zero optimal weights. Then these non-zero optimal weights would be aggregated to be the common weights using Shannon’s entropy. Compared with the traditional models, our proposed method is more powerful in discriminating DMUs, especially when the inputs and outputs are numerous. Our proposed method also keeps in accordance with the basic DEA method considering the evaluation of the most efficient and inefficient DMUs. Numerical examples are provided to examine the validity and effectiveness of our proposed methodology. View Full-Text
Keywords: data envelopment analysis; entropy; common weights; Shannon’s entropy data envelopment analysis; entropy; common weights; Shannon’s entropy
MDPI and ACS Style

Qi, X.-G.; Guo, B. Determining Common Weights in Data Envelopment Analysis with Shannon’s Entropy. Entropy 2014, 16, 6394-6414.

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