نتایج جستجو برای: ordered weighted averaging operators
تعداد نتایج: 264248 فیلتر نتایج به سال:
The main focus of this paper is to investigate the multiple attribute decision making (MADM) method under intuitionistic linguistic (IL) environment, based on induced aggregation operators and analyze possibilities for its application in low carbon supplier selection. More specifically, a new aggregation operator, called intuitionistic linguistic weighted induced ordered weighted averaging (ILW...
Trapezoidal intuitionistic fuzzy numbers (TrIFNs) express abundant and flexible information in a suitable manner and are very useful to depict the decision information in the procedure of decision making. In this paper, some new aggregation operators, such as, trapezoidal intuitionistic fuzzy weighted power harmonic mean (TrIFWPHM) operator, trapezoidal intuitionistic fuzzy ordered weighted po...
In the context of multiple attribute decision making, we present an aggregation scheme based on generalized mixture operators using weighting functions and we compare it with two standard aggregation methods: weighted averaging (WA) and ordered weighted averaging (OWA). Specifically, we consider linear and quadratic weight generating functions that penalize bad attribute performances and reward...
The diagnosed complex Pythagorean fuzzy (CPF) set is a more valuable and dominant tool than the intuitionistic sets to describe awkward unreliable information effectively. Further, Archimedean t-norm t-conorm have significant influence depict relation among aggregated values. To take advantage of CPF t-conorm, assume norms algebraic, Einstein, Hamacher, frank at same time, in this analysis, fir...
The hybrid averaging (HA) is an aggregation operator that uses the weighted average (WA) and the ordered weighted averaging (OWA) operator in the same formulation. In this paper, we introduce several generalizations of the HA operator by using generalized and quasi-arithmetic means, fuzzy numbers and order inducing variables in the reordering step of the aggregation process. We present the fuzz...
In [28] Yager and Filev introduced the Induced Ordered Weighted Averaging (IOWA) operator. In this paper, we provide some IOWA operators to aggregate fuzzy preference relations in group decision-making (GDM) problems. These IOWA operators when guided by fuzzy linguistic quantifiers allow the introduction of some semantics or meaning in the aggregation, and therefore allow for a better control o...
Many multiple attribute group decision making (MAGDM) problems with linguistic information involve uncertainties, which significantly increase the complexity and difficulty in decision analysis. In order to solve such problems, we require appropriate aggregation operators that are capable of dealing with uncertain linguistic information. In this paper, firstly, the uncertain two-tuple linguisti...
The ordered weighted geometric (OWG) operator is an aggregation operator that is based on the ordered weighted averaging (OWA) operator and the geometric mean. Its application in multicriteria decision making (MCDM) under multiplicative preference relations has been presented. Some families of OWG operators have been defined. In this article, we present the origin of the OWG operator and we rev...
In this paper, we look at the classical problem of aggregating individual utilities and study social orderings which are based on the concept of Ordered Weighted Averaging (OWA) Aggregating Operator. In these social orderings, called OWA social welfare functions (swf), weights are assigned a priori to the positions in the social ranking and, for every possible alternative, the total welfare is ...
This paper addresses the blog distillation problem, that is, given a user query find the blogs that are most related to the query topic. We model each post as evidence of the relevance of a blog to the query, and use aggregation methods like Ordered Weighted Averaging (OWA) operators to combine the evidence. We show that using only highly relevant evidence (posts) for each blog can result in an...
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