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Abstract
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In the present study, a basic two-stage system for uncertain conditions was evaluated. A key factor in DEA studies is the
use of accurate measurement of all factors. However, in many cases, such as the release of carbon dioxide and the
production system, inputs and outputs are very volatile. Volatile cases commonly were studied by using pre-known
information in which fuzzy theory or probability theory was applied. In the absence of knowledge for probability and fuzzy
theories, it is possible to use an expert’s belief degrees for modeling. For such cases, uncertainty theory can be applied
which was introduced by Liu (Uncertainty theory, Springer, Berlin, 2007). In this study, a basic two-stage model was
extended to consider uncertain conditions. Several theorems were presented to discuss some features of the introduced
model. When the data had a linear distribution, the efficiencies of the system and sub-system were calculated. Finally, a
numerical example was presented to illustrate the proposed model.
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