Comprehensive intuitionistic fuzzy network data envelopment analysis incorporating undesirable outputs and shared
Mohammad Aqil Sahil1, Q M Danish Lohani1
1South Asian University, Maidan Garhi, New Delhi, 110068, Delhi, India.
Methodsx
|April 25, 2024
Summary
This study enhances banking efficiency analysis using a novel network Data Envelopment Analysis (DEA) model. It addresses uncertainty with fuzzy numbers, offering better decision-making for banks.
Area of Science:
- Operations Research
- Financial Management
- Applied Mathematics
Background:
- Banking efficiency is crucial for economic growth.
- Traditional Data Envelopment Analysis (DEA) has limitations in handling internal processes and imprecise data.
- Existing models often treat Decision-Making Units (DMUs) as black boxes.
Purpose of the Study:
- To develop a comprehensive network two-stage DEA model for assessing banking efficiency.
- To incorporate shared inputs, undesirable outputs, and external factors into the efficiency assessment.
- To extend the model to intuitionistic fuzzy environments to manage data uncertainty.
Main Methods:
- A network two-stage DEA model with shared inputs, intermediate measures, undesirable outputs, and external factors was developed.
- The model was extended using parabolic intuitionistic fuzzy numbers to handle higher-order imprecise datasets.
- The methodology was validated with an illustrative example and compared against existing methods.
Main Results:
- The proposed network two-stage DEA model effectively assesses the efficiency of banking systems.
- The use of parabolic intuitionistic fuzzy numbers successfully addresses uncertainty in efficiency measurements.
- The methodology provides optimal efficiency values for improved decision-making.
Conclusions:
- The developed network two-stage DEA model offers a more realistic and comprehensive approach to efficiency assessment in the banking sector.
- The application to Indian public sector banks demonstrates the practical utility and effectiveness of the proposed methodology.
- This research provides valuable insights for financial decision-makers to optimize banking operations.
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