Traditional DEA models do not deal with imprecise data and assume that the data for all inputs and outputs are known exactly. Inverse DEA models can be used to estimate inputs for a DMU when some or all outputs and efficiency level of this DMU are increased or preserved. this paper studies the inverse DEA for fuzzy data. This paper proposes generalized inverse DEA in fuzzy data envelopment anal...

In this paper, we reformulate the conventional DEA models as an imprecise problem and propose a novel method for evaluating DMUs when inputs outputs are fuzzy and/or ordinal or vary in intervals. For purpose, convert all data into interval data. order to each number data, use nearest weighted approximation of numbers by applying weighting function, one. manner, could The presented determine eff...

The classification of fuzzy uncertain data is considered one of the most challenging issues in data analysis. In spite of the significance of fuzzy data in mathematical programming, the development of the analytical methods of fuzzy data is slow. Therefore, the current study proposes a new fuzzy data classification method based on fuzzy data envelopment analysis (DEA) which can handle strea...

The classification of fuzzy uncertain data is considered one of the most challenging issues in data analysis. In spite of the significance of fuzzy data in mathematical programming, the development of the analytical methods of fuzzy data is slow. Therefore, the current study proposes a new fuzzy data classification method based on fuzzy data envelopment analysis (DEA) which can handle strea...

Due to the ambiguity of the results given by the referee in Martial art competition, the design of a set of fair and impartial, scientific and rational evaluation system is particularly important. This paper builds a fuzzy comprehensive evaluation model based on the fuzzy number theory. As this model has a high degree of subjectivity, this article combines RBF neural network model and data enve...

Performance evaluation in conventional data envelopment analysis (DEA) requires crisp numerical values. However, the observed values of the input and output data in real-world problems are often imprecise or vague. These imprecise and vague data can be represented by linguistic terms characterised by fuzzy numbers in DEA to reflect the decision-makers' intuition and subjective judgements. This ...