Data envelopment analysis based on triangular neutrosophic numbers. Issue 2 (9th April 2020)

• Record Type: Journal Article
• Authors: Edalatpanah, S.A.
• Is Part Of: CAAI transactions on intelligence technology. Volume 5:Issue 2(2020)
• Page Start: 94
• Page End: 98
• Publication Date: 2020-04-09
• Subjects: fuzzy set theory -- decision making -- data envelopment analysis; uncertainty tools -- triangular neutrosophic information -- ranking functions -- comparative TNNs -- conventional DEA model -- decision‐making units -- crisp number -- mathematical techniques -- triangular neutrosophic numbers -- data envelopment analysis; C1160 Combinatorial mathematics -- C1290Z Other applications of systems theory; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2020.0016
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : Data envelopment analysis (DEA) is one of the best mathematical techniques to compute the overall performance of units with some inputs and outputs. The original DEA methods are developed to tackle the information based on the crisp number but no ability to handle the indeterminacy, impreciseness, vagueness, inconsistent, and incompleteness information such as triangular neutrosophic numbers (TNNs). This study attempts to establish a new model of DEA, where the information on decision‐making units is TNNs. Initially, the concept and features of a conventional DEA model and the comparative TNNs are discussed. Besides, some new ranking functions of TNNs are presented. Furthermore, based on the mentioned ranking functions, an algorithm for solving the new model has been established. A comparison of the new model with an existing method and other kinds of uncertainty tools has been provided. In comparison with the existing methods, the significant characteristic of the new model is that it can handle the triangular neutrosophic information simply and effectively. Finally, the implementation of this strategy for an example has been applied for various models of DEA.