Fuzzy efficiency measures in data envelopment analysis
Fuzzy Sets and Systems
Fuzzy DEA: a perceptual evalution method
Fuzzy Sets and Systems
Data envelopment analysis with imprecise data: an application of Taiwan machinery firms
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
A fuzzy DEA/AR approach to the selection of flexible manufacturing systems
Computers and Industrial Engineering
Fuzzy efficiency measures in fuzzy DEA/AR with application to university libraries
Expert Systems with Applications: An International Journal
Interval efficiency assessment using data envelopment analysis
Fuzzy Sets and Systems
Computers and Industrial Engineering
A robust optimization approach for imprecise data envelopment analysis
Computers and Industrial Engineering
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
A systematic decision-making approach for the optimal product-service system planning
Expert Systems with Applications: An International Journal
Fuzzy data envelopment analysis: A fuzzy expected value approach
Expert Systems with Applications: An International Journal
A geometrical approach for fuzzy DEA frontiers using different T norms
WSEAS TRANSACTIONS on SYSTEMS
A concept of fuzzy input mix-efficiency in fuzzy DEA and its application in banking sector
Expert Systems with Applications: An International Journal
International Journal of Fuzzy System Applications
Computers and Industrial Engineering
Fuzzy rough DEA model: A possibility and expected value approaches
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Data envelopment analysis (DEA) requires input and output data to be precisely known. This is not always the case in real applications. This paper proposes two new fuzzy DEA models constructed from the perspective of fuzzy arithmetic to deal with fuzziness in input and output data in DEA. The new fuzzy DEA models are formulated as linear programming models and can be solved to determine fuzzy efficiencies of a group of decision-making units (DMUs). An analytical fuzzy ranking approach is developed to compare and rank the fuzzy efficiencies of the DMUs. The proposed fuzzy DEA models and ranking approach are applied to evaluate the performances of eight manufacturing enterprises in China.