Uncertain data envelopment analysis with imprecisely observed inputs and outputs

Waichon Lio () and Baoding Liu ()
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Waichon Lio: Tsinghua University
Baoding Liu: Tsinghua University

Fuzzy Optimization and Decision Making, 2018, vol. 17, issue 3, No 6, 357-373

Abstract: 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.

Keywords: Data envelopment analysis; Uncertainty theory; Uncertain variable; Sensitivity and stability analysis (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (10)

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DOI: 10.1007/s10700-017-9276-x

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