基于模糊松弛测度(SBM)的DEA模型可信性研究

IF 1.9 4区 数学 Q1 MATHEMATICS
D. Mahla, S. Agarwal
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引用次数: 1

摘要

数据包络分析(DEA)是一种基于线性规划的多准则技术,用于处理许多现实问题,主要是在非营利组织中。基于松弛测度(slack -based measure, SBM)模型是用于评估决策单元相对效率的DEA模型之一。SBM DEA模型直接使用输入松弛和输出松弛来确定dmu的相对效率。为了处理定性或不确定的数据,本研究采用模糊SBM DEA模型来评估dmu的性能。采用可信度度量方法,将模糊SBM DEA模型在不同可信度水平上转化为清晰的线性规划模型。与传统的DEA模型相比,模糊DEA模型的结果更符合实际情况。最后,收集了印度炼油厂的数据,并应用所提出的模型对企业的效率行为进行了数值说明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A credibility approach on fuzzy slacks based measure (SBM) DEA model
Data Envelopment Analysis (DEA) is a multi-criteria technique based on linear programming to deal with many real-life problems, mostly in nonprofit organizations. The slacks-based measure (SBM) model is one of the DEA model used to assess the relative efficiencies of decision-making units (DMUs). The SBM DEA model directly used input slacks and output slacks to determine the relative efficiency of DMUs. In order to deal with qualitative or uncertain data, a fuzzy SBM DEA model is used to assess the performance of DMUs in this study. The credibility measure approach, transform the fuzzy SBM DEA model into a crisp linear programming model at different credibility levels is used. The results came from the fuzzy DEA model are more rational to the real-world situation than the conventional DEA model. In the end, the data of Indian oil refineries is collected, and the efficiency behavior of the companies obtained by applying the proposed model for its numerical illustration.
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来源期刊
CiteScore
3.50
自引率
16.70%
发文量
0
期刊介绍: The two-monthly Iranian Journal of Fuzzy Systems (IJFS) aims to provide an international forum for refereed original research works in the theory and applications of fuzzy sets and systems in the areas of foundations, pure mathematics, artificial intelligence, control, robotics, data analysis, data mining, decision making, finance and management, information systems, operations research, pattern recognition and image processing, soft computing and uncertainty modeling. Manuscripts submitted to the IJFS must be original unpublished work and should not be in consideration for publication elsewhere.
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