 # final have more weight in the aggregation method. They

final aggregated extent specify the value of the majority of the most similar amounts. The similarities between pairs of preference values can be computed using a support function, Sup (a, b), which can be denoted as the support for ‘a’ from ‘b’ where 19:   ,(1)Therefore, the closer that two argument values are, the more they support each other 20.  In addition, the most supported values have more weight in the aggregation method. They demonstrated that the fuzzy majority procedure produces the majority semantic of preferences in a group decision-making process 19.In this way, the score of using OWA achieved for each proposed option defined as Pk (k is decision makers no). The problem here can be defined as    which denotes the aggregation of the set preferences    in such a way that the final score corresponds to the majority of the preferences values.This problem can be solved by considering all the k values. At this stage the majority of fuzzy method involves the following steps:Calculating the total value support for the kth decision-maker’s preference from other decision-makers on the ith alternative as follows: tk =     ,(2)Where  is a binary support function as =    ,(3)Here weight Pk is calculated using OWA (or other forms of decision).According to the support function, , two values support each other if the proximity or distance between the two values is less than ‘?’; otherwise, they provide no support. In this setting, ‘?’ defines a threshold by which the proximity of the values is considered supportive (close enough) or not . From sum of these values up in a row, achieved the number t’k  the second stage of this sort in ascending order numbers t’k and t’1 ? t’2 ? … ? t’q is named.Define ‘Q’ as fuzzy membership function of “most” is calculated according to the following steps.Q?most? (x) =     (4),After calculating the value of ‘Q’, the Vk achieved according to equation (8) by dividing each value of ‘Q’ by the Total column values ‘Q’:Vk =         ,(5)Calculating the OWA score for each ith location, Ai, corresponding to the majority of the decision-makers’ preferences as follows:     ,(6)Where t-index (k) is the k th ‘smallest’ t.4: the final appropriate locations are determined and evaluated.

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