Abstract
The applications of fuzzy analysis in data-oriented techniques are the challenging aspect in the field of applied operational research. The use of fuzzy set theoretic measure is explored here in the context of data envelopment analysis (DEA) where we are utilizing the fuzzy α-level approach in the three types of efficiency models. Namely, BCC models, SBM model and supper efficiency model in DEA. It was observed from the result that the fuzzy SBM model has good discrimination power over fuzzy BCC. On the other side, both the models fuzzy BCC and fuzzy SBM are not able to make the genuine ranking which is acceptable for all. So this weakness is overcome with the help of fuzzy super SBM model and all three models are applied to illustrate the types of decisions and solutions that are achievable when the data are vague and prior information is in imprecise.
 In this paper, we are considering that our inputs and outputs are not known with absolute precision in DEA and here, we using Fuzzy-DEA models based on an α-level fuzzy approach to assessing fuzzy data.
Highlights
Since data envelopment analysis (DEA) was proposed in 1978 and after that it has been got comprehensive attention in both theories as well as in application
DEA is based on the production process and the data of production processes cannot be precisely measured always since the uncertain theory has played an important role in the inputs and outputs
The possibility approach is based on the fundamental principle of the possibility theory was imitated by Zadah (1977) and it was related to the theory of fuzzy sets by defining the concept of a possibility distribution as a fuzzy restriction which acts as an elastic constraint on the values that may be assigned to a variable
Summary
Since DEA was proposed in 1978 and after that it has been got comprehensive attention in both theories as well as in application. The rationale behind the selection of α-level approach in this study is related to a number of aspects When using this approach, fuzzy inputs and outputs may be expressed as crisp numbers representing the limiting bounds of the intervals for different α-levels in Chen et al (2013), allowing the uncertainty of the data collected from Mozambican banks to be modelled as triangular fuzzy numbers. As long as the efficiency values considered here are the upper and lower “crisp” bounds computed for various α levels, the membership functions for the true fuzzy efficiency cannot be reconstructed, which has a number of implications on how fuzzy efficiencies should be ranked in Chen et al( 2013); Puri and Yadav (2013); Hsiao et al (2011) These bounds, can be treated as crisp values and incorporated into statistical modelling as efficiency scores subjected to certain fixed. Effects or treatments in order to properly assess the impact of different contextual variables
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
