2-Dimension Linguistic Bonferroni Mean Aggregation Operators and Their Application to Multiple Attribute Group Decision Making

Autor:	Jianbin Zhao, Hua Zhu
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	MAGDM Bonferroni mean operator 2-dimension linguistic weight Bonferroni mean aggregation operator 2-dimension linguistic variable 2-dimension linguistic lattice implication algebra Electronic computers. Computer science QA75.5-76.95
Zdroj:	International Journal of Computational Intelligence Systems, Vol 12, Iss 2 (2019)
Druh dokumentu:	article
ISSN:	1875-6883
DOI:	10.2991/ijcis.d.191125.001
Popis:	The aim of this paper is to provide a multiple attribute group decision making (MAGDM) method based on the 2-dimension linguistic weight Bonferroni mean aggregation (2DLWBMA) operator. Firstly, the new operations of 2-dimension linguistic variables are defined. Then, the 2-dimension linguistic Bonferroni mean aggregation operator is proposed to describe the correlations of input arguments. Subsequently, the 2DLWBMA operator is investigated to consider the importance of attributes. Furthermore, a novel MAGDM method is introduced and two illustrative examples are given.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/7d5f94147cb84318bbfc53f217e3c363 Zobrazit plný text záznamu Full text from SpringerLink View record in DOAJ