| Summary: | The aim of this thesis is to investigate and identify the main effect of qualitative and
imprecise data, such as knowledge, experience and human judgments, in efficiency
evaluation of the real world problems on one hand, and non-discretionary data such as
environmental factors in Data Envelopment analysis issues (DEA) on the other hand.
In the first part, a fuzzy weighed CCR model is represented to assess decision making
units (DMUs) with normal data and fuzzy essence in weights of input and output factors.
In this case, incorporation of fuzzy arithmetic and traditional method in DEA is helpful
to solve and achieve the interval efficiency scores without additional restrictions for
each data which prevents the model from becoming infeasible. The outcome of the
model demonstrates the effect of data on the efficiency score.
This thesis then offers a non-discretionary fuzzy additive model for cases in efficiency
analysis and benchmarking that, decision-maker is confronted with some exogenously
fixed data with fuzzy essence. A detailed procedure on a method applying -cut concept
is provided to linearize the model with the intention of achieving appropriate
benchmarking for inefficient DMUs.
In conclusion, the thesis argues that impact of imprecise weights and non-discretionary
factors in DEA is essential and requires new modified models. The models offer more
informative and persuasive data to assist the manager to be aware of uncertain effects of
factors on efficiency score as well as to evaluate and benchmark DMUs with fuzzy
nature while non-discretionary factors are taken into account.
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