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Uncertain data envelopment analysis with imprecisely observed inputs and outputs

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Abstract

Data envelopment analysis (DEA) is a powerful analytical tool in operations research and management for measuring and estimating the efficiency of decision-making units. Both the inputs and the outputs are assumed to be known constants in the classical DEA models. However, in many cases, those data (e.g., carbon emissions and social benefit) cannot be measured in a precise way. Therefore, in this article, the inputs and outputs are considered as uncertain variables and a new uncertain DEA model is introduced. The sensitivity and stability of the new model are also analyzed. Finally, a numerical example of the new model is documented.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 61573210).

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Authors and Affiliations

  1. Department of Mathematical Sciences, Tsinghua University, Beijing, 100084, China

    Waichon Lio & Baoding Liu

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  1. Waichon Lio
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  2. Baoding Liu
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Correspondence to Baoding Liu.

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Lio, W., Liu, B. Uncertain data envelopment analysis with imprecisely observed inputs and outputs. Fuzzy Optim Decis Making 17, 357–373 (2018). https://doi.org/10.1007/s10700-017-9276-x

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