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Approaches to determining the relative importance weights for cross-efficiency aggregation in data envelopment analysis

Author

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  • Y M Wang

    (Fuzhou University, Fuzhou, PR China)

  • S Wang

    (The University of Manchester, Manchester, UK)

Abstract

Cross-efficiency evaluation has been widely used for identifying the most efficient decision making unit (DMU) or ranking DMUs in data envelopment analysis (DEA). Most existing approaches for cross-efficiency evaluation are focused on how to determine input and output weights uniquely, but pay little attention to the aggregation process of cross-efficiencies and simply aggregate them equally without considering their relative importance. This paper focuses on aggregating cross-efficiencies by taking into consideration their relative importance and proposes three alternative approaches to determining the relative importance weights for cross-efficiency aggregation. Numerical examples are examined to show the importance and necessity of the use of relative importance weights for cross-efficiency aggregation and the most efficient DMU can be significantly affected by taking into consideration the relative importance weights of cross-efficiencies.

Suggested Citation

  • Y M Wang & S Wang, 2013. "Approaches to determining the relative importance weights for cross-efficiency aggregation in data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(1), pages 60-69, January.
    • Handle: RePEc:pal:jorsoc:v:64:y:2013:i:1:p:60-69
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    Citations

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    Cited by:

    1. Yan Huang & Xiao He & Shizhen He & Yongwu Dai, 2022. "Efficiency Evaluation of a Forestry Green Economy under a Multi-Dimensional Output Benefit in China—Based on Evidential Reasoning and the Cross Efficiency Model," Sustainability, MDPI, vol. 14(21), pages 1-25, October.
    2. Qiang Hou & Meiou Wang & Xue Zhou, 2018. "Improved DEA Cross Efficiency Evaluation Method Based on Ideal and Anti-Ideal Points," Discrete Dynamics in Nature and Society, Hindawi, vol. 2018, pages 1-9, April.
    3. Carrillo, Marianela & Jorge, Jesús M., 2018. "Integrated approach for computing aggregation weights in cross-efficiency evaluation," Operations Research Perspectives, Elsevier, vol. 5(C), pages 256-264.
    4. Klimberg, Ronald & Ratick, Samuel, 2023. "Benchmarking nursing homes using the Order Rated Effectiveness model," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    5. Yinsheng Yang & Qianwei Zhuang & Guangdong Tian & Silin Wei, 2018. "A Management and Environmental Performance Evaluation of China’s Family Farms Using an Ultimate Comprehensive Cross-Efficiency Model (UCCE)," Sustainability, MDPI, vol. 11(1), pages 1-25, December.
    6. Sisi Wu & Yelin Fu & K. K. Lai & W. K. John Leung, 2018. "A Weighted Least-Square Dissimilarity Approach for Multiple Criteria ABC Inventory Classification," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 35(04), pages 1-12, August.
    7. Cheng Peng & Xunbo Wu & Yelin Fu & Kin Keung Lai, 2017. "Alternative approaches to constructing composite indicators: an application to construct a Sustainable Energy Index for APEC economies," Operational Research, Springer, vol. 17(3), pages 747-759, October.
    8. Davtalab-Olyaie, Mostafa & Asgharian, Masoud, 2021. "On Pareto-optimality in the cross-efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 288(1), pages 247-257.
    9. Feng Li & Han Wu & Qingyuan Zhu & Liang Liang & Gang Kou, 2021. "Data envelopment analysis cross efficiency evaluation with reciprocal behaviors," Annals of Operations Research, Springer, vol. 302(1), pages 173-210, July.

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