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A modified DEA cross efficiency method with negative data and its application in supplier selection

Author

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  • Mehdi Soltanifar

    (Islamic Azad University)

  • Hamid Sharafi

    (Islamic Azad University)

Abstract

Basic Data Envelopment Analysis (DEA) models are designed for non-negative data. However, negative data is inevitably used in many real-world issues. Also, multiple units with a maximum relative performance score (equal to one) can be obtained due to the benevolent view of evaluating Decision Making Units (DMUs) consistent performance. Therefore, the researchers proposed ranking models to differentiate efficient units. Cross efficiency is one of the most useful tools for DMUs ranking in the DEA. There are two major drawbacks to implementing this process. First, it gives different results in the presence of other optimal solutions; second, it does not provide a compelling reason to use the arithmetic mean to aggregate the results of the cross efficiency matrix. In this paper, first a new non-radial model is proposed to evaluate the performance of DMUs in the presence of negative data and then based on this model a new secondary goal model is proposed to eliminate the first drawback in the cross efficiency method. Also, to solve the second drawback in this method, a hybrid Multi-Attribute Decision Making (MADM)-DEA process with the help of fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje method is proposed. Finally, to show the applicability of the proposed methods, the results are used to select the supplier in a real-world problem.

Suggested Citation

  • Mehdi Soltanifar & Hamid Sharafi, 2022. "A modified DEA cross efficiency method with negative data and its application in supplier selection," Journal of Combinatorial Optimization, Springer, vol. 43(1), pages 265-296, January.
    • Handle: RePEc:spr:jcomop:v:43:y:2022:i:1:d:10.1007_s10878-021-00765-7
    DOI: 10.1007/s10878-021-00765-7
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    4. He Huang & Liwei Zhong & Ting Shen & Huixin Wang, 2022. "Performance prediction and optimization for healthcare enterprises in the context of the COVID-19 pandemic: an intelligent DEA-SVM model," Journal of Combinatorial Optimization, Springer, vol. 44(5), pages 3778-3791, December.

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