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An Intuitionistic Fuzzy Data Envelopment Analysis for Efficiency Evaluation Under Ucertainty: Case of a Finance and Credit Institution


Finance

An Intuitionistic Fuzzy Data Envelopment Analysis for Efficiency Evaluation Under Ucertainty: Case of a Finance and Credit Institution

Name and surname of author:

Seyed Hossein Razavi Hajiagha, Hadi Akrami, Edmundas Kazimieras Zavadskas, Shide Sadat Hashemi

Year:
2013
Volume:
16
Issue:
1
Keywords:
performance evaluation; data envelopment analysis; BCC model; intuitionistic fuzzy sets; aggregation operator
DOI (& full text):
Anotation:
Performance evaluation is a challenging issue for managers. Data envelopment analysis is a non parametric and linear programming based approach to appraise the relative efficiency of a set of congruent units. One of the shortcomings of classic data envelopment model is their crispness of data. In this paper, a data envelopment model is extended in which inputs and outputs are ambiguous and are expressed in the form of intuitionistic fuzzy sets. The proposed method is extended based on a weighted aggregation operator which is defined for intuitionistic fuzzy data. This model applied the advantages of intuitionistic fuzzy data in capturing the uncertainty. The main advantages of the proposed method are its simplicity and consistency with classic models. The proposed method is applied in a real instance and its results are examined.

Performance evaluation is a challenging issue for managers. Data envelopment analysis is a non parametric and linear programming based approach to appraise the relative efficiency of a set of congruent units. One of the shortcomings of classic data envelopment model is their crispness of data. In this paper, a data envelopment model is extended in which inputs and outputs are ambiguous and are expressed in the form of intuitionistic fuzzy sets. The proposed method is extended based on a weighted aggregation operator which is defined for intuitionistic fuzzy data.

This model applied the advantages of intuitionistic fuzzy data in capturing the uncertainty. The main advantages of the proposed method are its simplicity and consistency with classic models. The proposed method is applied in a real instance and its results are examined.
Section:
Finance

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