基于毕达哥拉斯方法的新颖正弦模糊数据包络分析模型:印度公共部门银行评估

IF 1.9 4区 经济学 Q2 ECONOMICS Computational Economics Pub Date : 2024-04-26 DOI:10.1007/s10614-024-10603-7
Mohammad Aqil Sahil, Meenakshi Kaushal, Q. M. Danish Lohani
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摘要

模糊数据包络分析(Fuzzy Data Envelopment Analysis)是一种建模技术,可根据不精确的输入和输出对决策单元(DMU)进行有效排名。该方法构建了一条高效前沿线,将高效和低效的 DMU 区分开来。其目标是通过将每个低效 DMU 移至高效前沿来提高它们的效率得分。在本研究中,我们引入了一种新方法,即毕达哥拉斯方法,它同时考虑了投入和产出两个方面。该方法被应用于 CCR 模型,并引入了一个新版本的 BCC 模型,即基于毕达哥拉斯方法的 BCC 模型。为了处理数据集的模糊性,基于勾股定理方法的 BCC 模型被扩展到模糊环境中,使用了一种新型模糊数,称为正弦形模糊数。最后,在印度公共部门银行中测试了该模型的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A Novel Pythagorean Approach Based Sine-Shaped Fuzzy Data Envelopment Analysis Model: An Assessment of Indian Public Sector Banks

Fuzzy Data Envelopment Analysis is a modeling technique that efficiently ranks decision-making units (DMUs) based on imprecise inputs and outputs. The method constructs an efficient frontier line that separates efficient and inefficient DMUs. The goal is to improve the efficiency score of each inefficient DMU by moving them to the efficient frontier. In this study, we introduce a new approach, called the Pythagorean approach, which considers both the input and the output aspects. The approach is applied to the CCR model, and a new version of the BCC model is introduced, known as the Pythagorean approach-based BCC model. To handle the vagueness of the data set, the Pythagorean approach-based BCC model is extended to a fuzzy environment using a new type of fuzzy number called a sine-shaped fuzzy number. Finally, the efficacy of the model is tested in Indian public sector banks.

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来源期刊
Computational Economics
Computational Economics MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.00
自引率
15.00%
发文量
119
审稿时长
12 months
期刊介绍: Computational Economics, the official journal of the Society for Computational Economics, presents new research in a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems from all branches in economics. The topics of Computational Economics include computational methods in econometrics like filtering, bayesian and non-parametric approaches, markov processes and monte carlo simulation; agent based methods, machine learning, evolutionary algorithms, (neural) network modeling; computational aspects of dynamic systems, optimization, optimal control, games, equilibrium modeling; hardware and software developments, modeling languages, interfaces, symbolic processing, distributed and parallel processing
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