An Alternative Approach to Rank Efficient DMUs in DEA via Cross-Efficiency Evaluation, Gini Coefficient, and Bonferroni Mean

doi:10.1007/s40305-019-00264-x

Abstract

Abstract: In this paper, we focus on a critical problem in data envelopment analysis (DEA) and propose a simple resolution for it. The major problem of the DEA is the existence of several efficient decision-making units (DMUs). To deal with this issue, we introduce a method that involves cross-efficiency evaluation, Gini coefficient, and Bonferroni mean. First, a cross-efficiency matrix is developed. Then, mixing the Gini coefficient and Bonferroni mean, a Gini–Bonferroni (GB) index is proposed for ranking efficient DMUs, where the DMUs with bigger GB are ranked higher. The proposed method broke the tie between efficient DMUs. Finally, a numerical example and real application of this method are presented in the ranking of research and development (R&D) investment companies in the pharmaceutical and biotechnology industries.

Key words: Data envelopment analysis, Efficiency, Gini coefficient, Bonferroni mean, Cross-efficiency evaluation, Ranking, Efficient units

CLC Number:

90B50
97K80

Zahra Behdani, Majid Darehmiraki. An Alternative Approach to Rank Efficient DMUs in DEA via Cross-Efficiency Evaluation, Gini Coefficient, and Bonferroni Mean[J]. Journal of the Operations Research Society of China, 2022, 10(4): 763-783.

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