Multiple Attribute Decision Making method based on intuitionistic Dombi operators and its application in mutual fund evaluation

IF 1.2 4区 计算机科学 Q4 AUTOMATION & CONTROL SYSTEMS
C. Jana
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引用次数: 7

Abstract

In this paper, a new set of intuitionistic fuzzy aggregation operators have been introduced under the environment of intuitionistic fuzzy sets (IFSs). For this, firstly focused on some existing aggregation operators and then new operational rules known as Dombi operation have been proposed which make the advancement of flexibility behavior with the parameter. Based on Dombi operation laws, some new averaging and geometric aggregation operators namely, intuitionistic fuzzy Dombi weighted averaging, ordered weighted averaging and hybrid weighted averaging operator, classified as IFDWA, IFDOWA and IFDHWA operators respectively and intuitionistic fuzzy Dombi geometric, ordered weighted geometric and hybrid weighted geometric operators, labeled as IFDWG, IFDOWG and IFDHWG operators respectively have been proposed. Further, some properties such as idempotency, boundedness, monotonicity and commutative are investigated. Finally, a multi-attribute decision-making model has been developed for the proposed operators to select the best mutual fund for investment. The execution of the comparative study has been examined with the existing operators in this environment.
基于直觉Dombi算子的多属性决策方法及其在共同基金评价中的应用
在直觉模糊集的环境下,引入了一组新的直觉模糊聚合算子。为此,本文首先对现有的一些聚合算子进行了分析,然后提出了一种新的操作规则,即Dombi操作,该规则通过参数来提升柔性行为。基于Dombi运算规律,提出了直观模糊Dombi加权平均算子、有序加权平均算子和混合加权平均算子,分别归类为IFDWA、IFDOWA和IFDHWA算子,以及直观模糊Dombi几何、有序加权几何和混合加权几何算子,分别归类为IFDWG、IFDOWG和IFDHWG算子。进一步研究了幂等性、有界性、单调性和交换性等性质。最后,建立了一个多属性决策模型,以帮助运营商选择最佳的共同基金进行投资。在这种环境下,与现有的运营商进行了比较研究。
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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来源期刊
Archives of Control Sciences
Archives of Control Sciences Mathematics-Modeling and Simulation
CiteScore
2.40
自引率
33.30%
发文量
0
审稿时长
14 weeks
期刊介绍: Archives of Control Sciences welcomes for consideration papers on topics of significance in broadly understood control science and related areas, including: basic control theory, optimal control, optimization methods, control of complex systems, mathematical modeling of dynamic and control systems, expert and decision support systems and diverse methods of knowledge modelling and representing uncertainty (by stochastic, set-valued, fuzzy or rough set methods, etc.), robotics and flexible manufacturing systems. Related areas that are covered include information technology, parallel and distributed computations, neural networks and mathematical biomedicine, mathematical economics, applied game theory, financial engineering, business informatics and other similar fields.
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