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A Novel Weighting Method for Finding Common Weights in DEA

Author

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  • Qing Wang

    (School of Business, Tianjin University of Commerce, Tianjin 300134, P. R. China2Management Innovation and Evaluation Research Center (MIERC), Tianjin University of Commerce, Tianjin 300134, P. R. China)

  • Zhaojun Liu

    (School of Business, Tianjin University of Commerce, Tianjin 300134, P. R. China)

  • Yang Zhang

    (School of Business, Tianjin University of Commerce, Tianjin 300134, P. R. China2Management Innovation and Evaluation Research Center (MIERC), Tianjin University of Commerce, Tianjin 300134, P. R. China)

Abstract

In the traditional DEA model, each DMU maximizes its efficiency with the most favorable weights. This leads to flexibility and unreality of input and output weights. Subsequently, it is unfair to compare and rank the efficiencies of different DMUs obtained on the basis of these weights. In this paper, we propose a novel approach to determine a common set of weights with more consensus to evaluate and rank the performance of all DMUs by weighting the rescaled weights based on the degree of consensus, where the weights obtained from DEA are rescaled for comparison among DMUs. Moreover, to overcome the non-uniqueness of the weights, a novel secondary goal is developed based on the agreement between self-evaluation and peer-evaluation. In addition, the restriction of weights is taken into account to avoid trivial weights. Finally, an example of 14 international passenger airlines is used to illustrate the performance and credibility of our proposed method.

Suggested Citation

  • Qing Wang & Zhaojun Liu & Yang Zhang, 2017. "A Novel Weighting Method for Finding Common Weights in DEA," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(05), pages 1-21, October.
    • Handle: RePEc:wsi:apjorx:v:34:y:2017:i:05:n:s0217595917500270
    DOI: 10.1142/S0217595917500270
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    References listed on IDEAS

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    Cited by:

    1. Cui, Qiang & Jia, Zi-ke, 2023. "Measuring the dynamic airline energy efficiency with non-homogeneous structures," Energy, Elsevier, vol. 266(C).
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    3. Sabri Boubaker & T.D.Q. Le & T. Ngo & R. Manita, 2023. "Predicting the Performance of MSMEs: A Hybrid DEA-machine Learning Approach," Post-Print hal-04434027, HAL.
    4. Kim, Nam Hyok & He, Feng & Kwon, O Chol, 2023. "Combining common-weights DEA window with the Malmquist index: A case of China’s iron and steel industry," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).

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