Properties of the aggregation operators related with fuzzy relations

Peneva, Vania; Попчев, Иван

doi:10.1016/s0165-0114(03)00141-6

Cited by 44 publications

(16 citation statements)

References 15 publications

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“…, R m and R i j , i = j, is the new fuzzy relation according to (16) and since R i j = R ji , the number k of the new relations will be equal to the combinations of two elements over m, i.e., k = m(m−1) 1.2 . Aggregation operators (Peneva and Popchev 2003) uniting these k relations can be used after that to obtain the aggregation fuzzy relation giving a possibility to decide the choice or ranking problems.…”

Section: Approach For Aggregation Of Fuzzy Relations With Different Imentioning

confidence: 99%

Aggregation of fuzzy preference relations to multicriteria decision making

Peneva

Попчев

2007

Fuzzy Optim Decis Making

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Weighted aggregation of fuzzy preference relations on the set of alternatives by several criteria in decision-making problems is considered. Pairwise comparisons with respect to importance of the criteria are given in fuzzy preference relation as well. The aggregation procedure uses the composition between each two relations of the alternatives. The membership function of the newly constructed fuzzy preference relation includes t-norms and t-conorms to take into account the relation between the criteria importance. Properties of the composition and new relation, giving a possibility to make a consistent choice or to rank the alternatives, are proved. An illustrative numerical study and comparative examples are presented.

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Section: Approach For Aggregation Of Fuzzy Relations With Different Imentioning

confidence: 99%

Aggregation of fuzzy preference relations to multicriteria decision making

Peneva

Попчев

2007

Fuzzy Optim Decis Making

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“…Classification, modifications, properties and application domains of fuzzy aggregation operators are given in the literature, e.g. [1,3,9,19,23]. In Let us try to determine only one fuzzy set out of this interval, which takes proper account of the values of all the other members of the given finite collection of fuzzy sets.…”

Section: Proofmentioning

confidence: 99%

A method for fuzzy aggregation based on group expert evaluations

Tsabadze

2006

Fuzzy Sets and Systems

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“…It integrates information from higher dimensions to one dimension to facilitate an overall judgment in the decision-making procedure. Several kinds of aggregation operators have been discussed in the literature [3,9,11,22,26,31,32]. In essence, in these methods, aggregation 0020-0255/$ -see front matter Ó 2008 Elsevier Inc. All rights reserved.…”

Section: Introductionmentioning

confidence: 99%