{"title":"基于可能性均值法的中性数据包络分析","authors":"Kshitish Kumar Mohanta, Deena Sunil Sharanappa, Vishnu Narayan Mishra","doi":"10.37190/ord230205","DOIUrl":null,"url":null,"abstract":"Data envelopment analysis (DEA) is a non-parametric approach for the estimation of production frontier that is used to calculate the performance of a group of similar decision-making units (DMUs) which employ comparable inputs to produce related outputs. However, observed values might occasionally be confusing, imprecise, ambiguous, inadequate, and inconsistent in real-world applications. Thus, disregarding these factors may result in incorrect decision-making. Thus neutrosophic sets have been created as an extension of intuitionistic fuzzy sets to represent ambiguous, erroneous, missing, and inaccurate information in real-world applications. In this study, we have proposed a technique for solving the neutrosophic form of the Charnes–Cooper–Rhodes (CCR) model based on single-value trapezoidal neutrosophic numbers (SVTrNNs). The possibilistic mean for SVTrNNs is redefined and applied the Mehar approach to transforming the neutrosophic DEA (Neu-DEA) model into its corresponding crisp DEA model. As a result, the efficiency scores of the DMUs are calculated using different risk parameter values lying in [0, 1]. A numerical example is given to analyze the performance of the all India institutes of medical sciences and compared it with Abdelfattah’s ranking approach.","PeriodicalId":43244,"journal":{"name":"Operations Research and Decisions","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Neutrosophic data envelopment analysis based on the possibilistic mean approach\",\"authors\":\"Kshitish Kumar Mohanta, Deena Sunil Sharanappa, Vishnu Narayan Mishra\",\"doi\":\"10.37190/ord230205\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data envelopment analysis (DEA) is a non-parametric approach for the estimation of production frontier that is used to calculate the performance of a group of similar decision-making units (DMUs) which employ comparable inputs to produce related outputs. However, observed values might occasionally be confusing, imprecise, ambiguous, inadequate, and inconsistent in real-world applications. Thus, disregarding these factors may result in incorrect decision-making. Thus neutrosophic sets have been created as an extension of intuitionistic fuzzy sets to represent ambiguous, erroneous, missing, and inaccurate information in real-world applications. In this study, we have proposed a technique for solving the neutrosophic form of the Charnes–Cooper–Rhodes (CCR) model based on single-value trapezoidal neutrosophic numbers (SVTrNNs). The possibilistic mean for SVTrNNs is redefined and applied the Mehar approach to transforming the neutrosophic DEA (Neu-DEA) model into its corresponding crisp DEA model. As a result, the efficiency scores of the DMUs are calculated using different risk parameter values lying in [0, 1]. A numerical example is given to analyze the performance of the all India institutes of medical sciences and compared it with Abdelfattah’s ranking approach.\",\"PeriodicalId\":43244,\"journal\":{\"name\":\"Operations Research and Decisions\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Operations Research and Decisions\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37190/ord230205\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"OPERATIONS RESEARCH & MANAGEMENT SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Operations Research and Decisions","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37190/ord230205","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
引用次数: 1
摘要
数据包络分析(DEA)是一种用于估计生产前沿的非参数方法,用于计算一组类似决策单元(dmu)的绩效,这些决策单元采用可比较的投入来产生相关的产出。然而,在实际应用程序中,观察到的值有时可能令人困惑、不精确、模棱两可、不充分和不一致。因此,忽视这些因素可能会导致错误的决策。因此,中性集作为直觉模糊集的扩展被创建,以表示现实世界应用中的模糊、错误、缺失和不准确的信息。在这项研究中,我们提出了一种基于单值梯形中性粒细胞数(SVTrNNs)的求解Charnes-Cooper-Rhodes (CCR)模型的中性粒细胞形式的技术。重新定义了svtrnn的可能性均值,并应用Mehar方法将嗜中性DEA (new -DEA)模型转化为相应的脆DEA模型。因此,采用[0,1]中不同的风险参数值计算dmu的效率得分。给出了一个数值例子,分析了印度所有医学科学研究所的表现,并将其与Abdelfattah的排名方法进行了比较。
Neutrosophic data envelopment analysis based on the possibilistic mean approach
Data envelopment analysis (DEA) is a non-parametric approach for the estimation of production frontier that is used to calculate the performance of a group of similar decision-making units (DMUs) which employ comparable inputs to produce related outputs. However, observed values might occasionally be confusing, imprecise, ambiguous, inadequate, and inconsistent in real-world applications. Thus, disregarding these factors may result in incorrect decision-making. Thus neutrosophic sets have been created as an extension of intuitionistic fuzzy sets to represent ambiguous, erroneous, missing, and inaccurate information in real-world applications. In this study, we have proposed a technique for solving the neutrosophic form of the Charnes–Cooper–Rhodes (CCR) model based on single-value trapezoidal neutrosophic numbers (SVTrNNs). The possibilistic mean for SVTrNNs is redefined and applied the Mehar approach to transforming the neutrosophic DEA (Neu-DEA) model into its corresponding crisp DEA model. As a result, the efficiency scores of the DMUs are calculated using different risk parameter values lying in [0, 1]. A numerical example is given to analyze the performance of the all India institutes of medical sciences and compared it with Abdelfattah’s ranking approach.