MCDM technique using single-valued neutrosophic trigonometric weighted aggregation operators

IF 3.6 2区管理学 Q2 BUSINESS

Journal of Management Analytics Pub Date : 2023-10-08 DOI:10.1080/23270012.2023.2264294

Jun Ye, Shigui Du, Rui Yong

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引用次数: 0

Abstract

AbstractMotivated based on the trigonometric t-norm and t-conorm, the aims of this article are to present the trigonometric t-norm and t-conorm operational laws of SvNNs and then to propose the SvNN trigonometric weighted average and geometric aggregation operators for the modelling of a multiple criteria decision making (MCDM) technique in an inconsistent and indeterminate circumstance. To realize the aims, this paper first proposes the trigonometric t-norm and t-conorm operational laws of SvNNs, which contain the hybrid operations of the tangent and arctangent functions and the cotangent and inverse cotangent functions, and presents the SvNN trigonometric weighted average and geometric operators and their properties. Next, a MCDM technique is proposed in view of the presented two aggregation operators in the circumstance of SvNNs. In the end, an actual case of the choice issue of slope treatment schemes is provided to indicate the practicability and effectivity of the proposed MCDM technique.Keywords: Single-valued neutrosophic numbertrigonometric t-norm and t-conormtrigonometric weighted aggregation operatordecision making Data availabilityAll data are included in this study.Disclosure statementNo potential conflict of interest was reported by the author(s).

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MCDM技术使用单值嗜中性三角加权聚合算子

摘要基于三角t-范数和t-保形，给出了SvNN的三角t-范数和t-保形运算规律，并提出了用于不一致不确定情况下多准则决策(MCDM)建模的SvNN三角加权平均算子和几何聚集算子。为了实现这一目标，本文首先提出了SvNN的三角t范数和t保形运算定律，其中包含了正切函数和反正切函数以及余切函数和反切函数的混合运算，并给出了SvNN的三角加权平均算子和几何算子及其性质。然后，针对上述两种聚合算子，在svnn环境下提出了一种MCDM技术。最后，给出了一个边坡治理方案选择问题的实际案例，说明了所提出的MCDM技术的实用性和有效性。关键词:单值中性数三角t-范数和t-共形三角加权聚集算子决策数据可用性本研究包含所有数据。披露声明作者未报告潜在的利益冲突。

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来源期刊

Journal of Management Analytics SOCIAL SCIENCES, MATHEMATICAL METHODS-

CiteScore

13.30

自引率

3.40%

发文量

期刊介绍： The Journal of Management Analytics (JMA) is dedicated to advancing the theory and application of data analytics in traditional business fields. It focuses on the intersection of data analytics with key disciplines such as accounting, finance, management, marketing, production/operations management, and supply chain management. JMA is particularly interested in research that explores the interface between data analytics and these business areas. The journal welcomes studies employing a range of research methods, including empirical research, big data analytics, data science, operations research, management science, decision science, and simulation modeling.