Development of Intuitionistic Fuzzy Revenue Efficiency Models in DEA: An Application to the Indian Public Sector Banks

Anjali Sonkariya () and Shiv Prasad Yadav
Additional contact information
Anjali Sonkariya: Indian Institute of Technology Roorkee
Shiv Prasad Yadav: Indian Institute of Technology Roorkee

A chapter in Advances in the Theory and Applications of Performance Measurement and Management, 2024, pp 137-149 from Springer

Abstract: Abstract Data envelopment analysis (DEA) is a non-parametric linear programming (LP) based technique to measure the relative efficiencies of homogeneous decision-making units (DMUs). The classical models of DEA rely on crisp input-output data, which may not always be available in real-life scenarios. Due to the existence of uncertainty and vagueness in real-life data, the concept of intuitionistic fuzzy (IF) has been introduced to handle imprecise data. Here, the conventional revenue efficiency models of DEA are extended to the IF environment. Also, the lower and upper revenue efficiency models are developed using $$\alpha $$ α and $$\beta $$ β -cuts approaches. The input-output data and output prices are considered triangular intuitionistic fuzzy numbers (TIFNs). An application to the public sector banks of India is provided to illustrate the practicality of the proposed intuitionistic fuzzy revenue efficiency models (IFREMs). Data is collected from the official website of the Reserve Bank of India (RBI), Govt. of India, India.

Keywords: $$\alpha $$ α and $$ \beta $$ β -cuts; Revenue efficiency; Data envelopment analysis; Intuitionistic fuzzy enviornment (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-61597-9_12

Ordering information: This item can be ordered from
http://www.springer.com/9783031615979

DOI: 10.1007/978-3-031-61597-9_12

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().