Data envelopment analysis (DEA) has been a very popular method for measuring and benchmarking relative efficiency of peer decision making units (DMUs) with multiple input and outputs. Traditional data envelopment analysis (DEA) models require crisp input and output data. In real world situations, however, crisp input and output data may not always be available, especially when a set of decision-making units (DMUs) contains missing data, judgment data, or predictive data. In this paper, a new model of Data Envelopment Analysis is provided to enable the user to decide the unit performance with regard to fuzziness data. The new fuzzy DEA models are formulated as LP models without the need of making any assumptions and too much computational effort. In addition, this paper, an analytical fuzzy ranking approach is developed to compare and rank the fuzzy efficiencies of the DMUs

Keywords: Data Envelopment Analysis, Fuzzy Input and Output, Fuzzy Efficiency, PDA Method.

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