A Multiple Attribute Group Decision Making Method Based on 2-D Uncertain Linguistic Weighted Heronian Mean Aggregation Operator

Liu, Weihua; Liu, Hai-Bo

doi:10.15837/ijccc.2017.2.2792

Cited by 10 publications

(6 citation statements)

References 3 publications

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“…By use of 2DLVs, the decision makers can better express their opinions. Up to now, the research achievements of 2-dimension linguistic information can be classified into three categories: 2DLVs [12][13][14][15][16][17], 2-dimension uncertain linguistic variables (2DULVs) [18][19][20][21][22][23][24][25][26][27][28], and hesitant fuzzy 2-dimension linguistic variables (HF2DLVs) [29,30]. Since there exist two classes of LVs in a 2DLV, the operations of 2DLVs become more difficult and complex than those of LVs.…”

Section: Introductionmentioning

confidence: 99%

“…Since there exist two classes of LVs in a 2DLV, the operations of 2DLVs become more difficult and complex than those of LVs. For simplicity of calculation, many existing 2DLV operations only take the minimum values of the II class LVs [12,[19][20][21]23,25,26,31,32]. Although it is easy to operate by taking minimum values, many useful linguistic information may be lost or distorted.…”

Section: Introductionmentioning

confidence: 99%

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

Zhao¹,

Zhu²

2019

IJCIS

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhao¹,

Zhu²

2019

IJCIS

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“…Decision-making is a common activity in daily life, aiming to select the best alternative from several candidates. As one of the most important branches of modern decision-making theory, multi-attribute group decision-making (MAGDM) has been widely investigated and successfully applied to economics and management due to its high capacity to model the fuzziness and uncertainty of information [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. In actual decision-making problems, decision-makers usually rely on their intuition and prior expertise to make decisions.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Multi-Attribute Group Decision-Making with q-Rung Picture Linguistic Information

et al. 2018

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The proposed q-rung orthopair fuzzy set (q-ROFS) and picture fuzzy set (PIFS) are two powerful tools for depicting fuzziness and uncertainty. This paper proposes a new tool, called q-rung picture linguistic set (q-RPLS) to deal with vagueness and impreciseness in multi-attribute group decision-making (MAGDM). The proposed q-RPLS takes full advantages of q-ROFS and PIFS and reflects decision-makers' quantitative and qualitative assessments. To effectively aggregate q-rung picture linguistic information, we extend the classic Heronian mean (HM) to q-RPLSs and propose a family of q-rung picture linguistic Heronian mean operators, such as the q-rung picture linguistic Heronian mean (q-RPLHM) operator, the q-rung picture linguistic weighted Heronian mean (q-RPLWHM) operator, the q-rung picture linguistic geometric Heronian mean (q-RPLGHM) operator, and the q-rung picture linguistic weighted geometric Heronian mean (q-RPLWGHM) operator. The prominent advantage of the proposed operators is that the interrelationship between q-rung picture linguistic numbers (q-RPLNs) can be considered. Further, we put forward a novel approach to MAGDM based on the proposed operators. We also provide a numerical example to demonstrate the validity and superiorities of the proposed method.

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“…A two-dimension uncertain linguistic variable (TDULV) is composed of two parts, which includes the Ι class and the ΙΙ class linguistic information, where the Ι class information represents the assessment of decision maker to the evaluated objects, and the ΙΙ class linguistic information denotes the reliability of the Ι class assessment denoted by the decision maker. Until now, the TDULVs have been applied in many areas, such as the technology innovation ability evaluation problem [12], the extraefficient economic industry system selection [13], and the river basin ecosystem health evaluation problem [14].…”

Section: Introductionmentioning

confidence: 99%

Power Battery Recycling Mode Selection Using an Extended MULTIMOORA Method

Ding

Zhong

2018

Scientific Programming

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In order to improve the efficiency of the recycling of the electric vehicle power batteries and reduce the recycling cost, it is of great importance to select an optimal power battery recycling mode. In this paper, an extended MULTIMOORA (Multiobjective Optimization by Ratio Analysis plus full Multiplicative form) approach which combines with the two-dimension uncertain linguistic variables (TDULVs) and the regret theory, called TDUL-RT-MULTIMOORA method, is developed for solving the power battery recycling mode decision-making (PBRMDM) problem. Firstly, the evaluations of the power battery recycling modes over criteria are given by the experts using the TDULVs, and the evaluations of all experts are aggregated into a group linguistic decision matrix by the TDULDWA operator. On the basis of the regret theory, the perceived utility decision matrix is constructed. And then, in order to avoid the disadvantages of the subjective weighting methods, such as the deviation from the measured data and the dependence on the experience and knowledge of the experts, an objective entropy weighting method is applied. After that, the MULTIMOORA method is introduced to rank the power battery recycling modes. In the end, an illustrative example is given to verify the effectiveness and practicability of the proposed method.

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A Multiple Attribute Group Decision Making Method Based on 2-D Uncertain Linguistic Weighted Heronian Mean Aggregation Operator

Cited by 10 publications

References 3 publications

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

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

A Novel Approach to Multi-Attribute Group Decision-Making with q-Rung Picture Linguistic Information

Power Battery Recycling Mode Selection Using an Extended MULTIMOORA Method

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