نتایج جستجو برای: fuzzy linguistic aggregation.
تعداد نتایج: 202637 فیلتر نتایج به سال:
In this paper, we investigate the multiple attribute decision making (MADM) problem based on the arithmetic and geometric aggregation operators with hesitant fuzzy linguistic information. Then, motivated by the idea of traditional arithmetic operation, we have developed some aggregation operators for aggregating hesitant fuzzy linguistic information: hesitant fuzzy linguistic weighted average (...
today’s world economy situation forces enterprise organizations toward more soft and flexible organization, management, and production processes. they need to explore the most suitable knowledge management (km) tool not only to identify gaps and overlaps but also to maintain and support innovation cross organizations. in this study, a multiple-experts-multiple-criteria decision making model is ...
In this paper, we investigate the multiple attribute decision making (MADM) problem based on the arithmetic and geometric aggregation operators with interval-valued dual hesitant fuzzy linguistic information. Then, motivated by the ideal of traditional arithmetic operation, we have developed some aggregation operators for aggregating interval-valued dual hesitant fuzzy linguistic information: i...
In this paper, we investigate multiple attribute group decision making (MAGDM) problems where decision makers represent their evaluation of alternatives by trapezoidal fuzzy two-dimensional uncertain linguistic variable. To begin with, we introduce the definition, properties, expectation, operational laws of trapezoidal fuzzy two-dimensional linguistic information. Then, to improve the accuracy...
The aim of fuzzy risk analysis is to evaluate the probability of failure of every component consisting of many sub-components and the probability of failure is the combination of estimations of severity of loss and probability of failure of sub-components which are vaguely known. In this paper, we present a new method for fuzzy risk analysis with linguistic evaluating values. Firstly, we propos...
Based on linguistic term sets and hesitant fuzzy sets, the concept of hesitant fuzzy linguistic sets was introduced. The focus of this paper is the multicriteria decision-making (MCDM) problems in which the criteria are in different priority levels and the criteria values take the form of hesitant fuzzy linguistic numbers (HFLNs). A new approach to solving these problems is proposed, which is b...
Information Retrieval Systems (IRSs) based on an ordinal fuzzy linguistic approach present some problems of loss of precision and information when working with discrete linguistic expression domains or when applying approximation operations in the symbolic aggregation methods. In this paper, we present an IRS based on a 2tuple fuzzy linguistic approach which allows us to overcome the problems o...
Information Retrieval Systems (IRSs) based on an ordinal fuzzy linguistic approach present some problems of loss of information and lack of precision when working with discrete linguistic expression domains or when applying approximation operations in the symbolic aggregation methods. In this paper, we present a new IRS model based on the 2-tuple fuzzy linguistic approach, which allows us to ov...
The intuitionistic uncertain fuzzy linguistic variable can easily express the fuzzy information, and the power average (PA) operator is a useful tool which provides more versatility in the information aggregation procedure. At the same time, Einstein operations are a kind of various t-norms and t-conorms families which can be used to perform the corresponding intersections and unions of intuiti...
A problem that we had encountered in the aggregation process, is how to aggregate the elements that have cardinality 1. The purpose of this article is to present a new aggregation operator of linguistic labels that uses the cardinality of these elements, the linguistic aggregation of majority additive (LAMA) operator. We also present an extension of the LAMA operator under the two-tuple fuzzy l...
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