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Induced unbalanced linguistic ordered weighted average and its application in multiperson decision making.

Marin L, Valls A, Isern D, Moreno A, Merigó JM - ScientificWorldJournal (2014)

Bottom Line: We propose a new order-inducing criterion based on the specificity and fuzziness of the linguistic terms.Different relevancies are given to the fuzzy values according to their uncertainty degree.To illustrate the behaviour of the precision-based IULOWA operator, we present an environmental assessment case study in which a multiperson multicriteria decision making model is applied.

View Article: PubMed Central - PubMed

Affiliation: Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Avinguda Països Catalans 26, 43007 Tarragona, Catalonia, Spain.

ABSTRACT
Linguistic variables are very useful to evaluate alternatives in decision making problems because they provide a vocabulary in natural language rather than numbers. Some aggregation operators for linguistic variables force the use of a symmetric and uniformly distributed set of terms. The need to relax these conditions has recently been posited. This paper presents the induced unbalanced linguistic ordered weighted average (IULOWA) operator. This operator can deal with a set of unbalanced linguistic terms that are represented using fuzzy sets. We propose a new order-inducing criterion based on the specificity and fuzziness of the linguistic terms. Different relevancies are given to the fuzzy values according to their uncertainty degree. To illustrate the behaviour of the precision-based IULOWA operator, we present an environmental assessment case study in which a multiperson multicriteria decision making model is applied.

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Examples of ULOWA aggregation of two labels (VL and P).
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig4: Examples of ULOWA aggregation of two labels (VL and P).

Mentions: Figure 4 depicts the aggregation procedure of the two extreme labels of an unbalanced 7-term set (VL and P) for different policies. The figure shows the COGs of both labels and their intermediate crisp number δ that is used to find the result of the aggregation when varying the vector W. Three usual aggregation policies have been used. After the similarity function is applied using (14), ULOWA obtains the neutral term M in the case of mean, H in the case of at least half, and L in the case of as many as possible.


Induced unbalanced linguistic ordered weighted average and its application in multiperson decision making.

Marin L, Valls A, Isern D, Moreno A, Merigó JM - ScientificWorldJournal (2014)

Examples of ULOWA aggregation of two labels (VL and P).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4127207&req=5

fig4: Examples of ULOWA aggregation of two labels (VL and P).
Mentions: Figure 4 depicts the aggregation procedure of the two extreme labels of an unbalanced 7-term set (VL and P) for different policies. The figure shows the COGs of both labels and their intermediate crisp number δ that is used to find the result of the aggregation when varying the vector W. Three usual aggregation policies have been used. After the similarity function is applied using (14), ULOWA obtains the neutral term M in the case of mean, H in the case of at least half, and L in the case of as many as possible.

Bottom Line: We propose a new order-inducing criterion based on the specificity and fuzziness of the linguistic terms.Different relevancies are given to the fuzzy values according to their uncertainty degree.To illustrate the behaviour of the precision-based IULOWA operator, we present an environmental assessment case study in which a multiperson multicriteria decision making model is applied.

View Article: PubMed Central - PubMed

Affiliation: Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Avinguda Països Catalans 26, 43007 Tarragona, Catalonia, Spain.

ABSTRACT
Linguistic variables are very useful to evaluate alternatives in decision making problems because they provide a vocabulary in natural language rather than numbers. Some aggregation operators for linguistic variables force the use of a symmetric and uniformly distributed set of terms. The need to relax these conditions has recently been posited. This paper presents the induced unbalanced linguistic ordered weighted average (IULOWA) operator. This operator can deal with a set of unbalanced linguistic terms that are represented using fuzzy sets. We propose a new order-inducing criterion based on the specificity and fuzziness of the linguistic terms. Different relevancies are given to the fuzzy values according to their uncertainty degree. To illustrate the behaviour of the precision-based IULOWA operator, we present an environmental assessment case study in which a multiperson multicriteria decision making model is applied.

Show MeSH
Related in: MedlinePlus