A new data envelopment analysis in fully fuzzy environment on the base of the degree of certainty of information

Data Envelopment Analysis (DEA) is a linear programming (LP) based technique for evaluating the comparative efficiency of decision-making units (DMUs) based on multiple inputs and outputs. Conventional DEA forms require precise data. However in real problems, it is not easy to measure inputs and outputs in an exact way to find precise data. Although, fuzzy set theory has been introduced as a powerful approach to quantify vague data and several authors have suggested various fuzzy DEA models, there is a key flaw in previous approaches. When coping with real information, fuzziness is not sufficient to consider and a reliability of the information is very vital too. A Z-number has extra ability to depict the uncertain information. This concept relates to the topic of reliability of information. Z-number can portray fuzziness and reliability of information concurrently. In this paper, we consider a different fuzzy DEA form, where all the inputs and outputs and also their weights are Z-numbers. This Z-numbers DEA model turned into fully fuzzy LP on the basis of fuzzy expectation. Finally, we transform the fully fuzzified DEA model to the classical LP model. This method has very straightforward calculations and the key benefit of the proposed method is its low computational intricacy.