Ordered visibility graph weighted averaging aggregation operator: A methodology based on network analysis

Wang, Hongping; Mo, Hongming; Sadiq, Rehan; Hu, Yong; Deng, Yong

doi:10.1016/j.cie.2015.06.021

Cited by 17 publications

(7 citation statements)

References 86 publications

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“…Furthermore,

0true F ((), a_{1}, a_{2}, \dots, a_{n}) = \sum_{j} w_{j} b_{j}

where

b_{j}

is the j th largest of the

a_{i}

. OWA operator is a simple and effective aggregating formulation in the data fusion, which has been applied in many applications, such as aggregating probability distribution, multicriteria decision making, emission control in green engineering, and complex network analysis …”

Section: Preliminariesmentioning

confidence: 99%

“…OWA operator is a simple and effective aggregating formulation in the data fusion, which has been applied in many applications, such as aggregating probability distribution, 31 multicriteria decision making, 32,33 emission control in green engineering, 34 and complex network analysis. 35 Definition 2.4. Orness measure 28 : orness measure is proposed by Yager to evaluate the decision maker's attitude, which is denoted by…”

Section: Definition 22 a Z-numbermentioning

confidence: 99%

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Generating Z-number based on OWA weights using maximum entropy

Kang

Deng

Hewage

et al. 2018

Int. J. Intell. Syst.

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In the application of Z‐number, how to generate Z‐number is a significant and open issue. In this paper, we proposed a method of generating Z‐number based on the OWA weights using maximum entropy considering the attitude (preference) of the decision maker. Some numerical examples are used to illustrate the effectiveness of the proposed method. Results show that the attitude (preference) of the decision maker can give an optimal possibility distribution of the reliability for Z‐number using maximum entropy.

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“…Furthermore,

0true F ((), a_{1}, a_{2}, \dots, a_{n}) = \sum_{j} w_{j} b_{j}

where

b_{j}

is the j th largest of the

a_{i}

Section: Preliminariesmentioning

confidence: 99%

Section: Definition 22 a Z-numbermentioning

confidence: 99%

Generating Z-number based on OWA weights using maximum entropy

Kang

Deng

Hewage

et al. 2018

Int. J. Intell. Syst.

Self Cite

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“…Since the appearance of the OWA operator, it has been widely used in various applications, 9–11 and many extensions have been developed about it 12–15 . The induced ordered weighted averaging (IOWA) operator is an interesting and important extension of the OWA operator, first proposed by Yager and Filev 16 .…”

Section: Introductionmentioning

confidence: 99%

Competitive behavior induced OWA operator and the weighting method

2021

Int J Intell Syst

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In view of the decision‐making problem that the alternatives actively participate in the decision‐making process and present competitive behavior, this paper proposes a new type of aggregation operator called the competitive behavior induced ordered weighted averaging (CB‐IOWA) operator. The primary characteristic of the CB‐IOWA operator is that the alternative is permitted to highlight its competitive advantage through the order‐inducing variables and the weighting vector. A method is introduced to determine the final weighting vector based on the weights provided by different alternatives that can reflect their competitive advantages. The final weights are calculated by maximizing the closeness degree between the final weights and the ones provided the alternatives. The main properties of the CB‐IOWA operator are discussed, such as commutativity, boundness, and monotonicity under certain condition. Finally, an application example regarding the competitive development of the economy‐environment of four cities in China is provided to illustrate the usage of the CB‐IOWA operator. The validity of the operator is checked by comparison with the weighted arithmetic averaging operator representing noncompetitive behavior.

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“…Then how to reduce negative effectiveness of these uncertainties? Various theories have been proposed to solve the problem, such as evidence theory [34], belief entropy [35]- [37], belief function [38], [39], fuzzy set theory [40], D-number [41], Z-number [42] and so on [43]. In this study, we try to address the problem from a perspective of fuzzy evaluation.…”

Section: Introductionmentioning

confidence: 99%

A Novel Failure Mode and Effects Analysis Method Based on Fuzzy Evidential Reasoning Rules

2019

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Failure mode and effects analysis (FMEA) is an effective reliability analysis technique and has been used for safety and dependability analysis in a wide range of fields. In the traditional FMEA, a method called risk priority number (RPN) has been widely used to determine the risk levels of failure modes. However, the method is deficient in dealing with imprecise data. To overcome that shortcoming, we propose a novel method based on fuzzy evidential reasoning rules to study the risk evaluation of failure modes in an uncertainty evaluation environment. The main contributions of this work are twofold: First, by analyzing the classical risk priority number method, we extract the reasoning knowledge from RPN method to construct fuzzy evidential reasoning rules for risk evaluation based on virtue of Dempster-Shafer evidence theory and fuzzy set theory; Second, the initial risk assessment is modeled with fuzzy form based on basic probability assignment (BPA) and fuzzy number, which can perfectly reflect the uncertainties in practice. The approach establishes a new reasoning model for fuzzy risk evaluation in FMEA. Finally, an example for risk evaluation of failure modes during general anesthesia process is given to illustrate the effectiveness of the proposed method. INDEX TERMS Dependability, failure mode and effect analysis, Dempster-Shafer evidence theory, fuzzy set theory, fuzzy evidential reasoning rule, risk priority number.

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Ordered visibility graph weighted averaging aggregation operator: A methodology based on network analysis

Cited by 17 publications

References 86 publications

Generating Z-number based on OWA weights using maximum entropy

Generating Z-number based on OWA weights using maximum entropy

Competitive behavior induced OWA operator and the weighting method

A Novel Failure Mode and Effects Analysis Method Based on Fuzzy Evidential Reasoning Rules

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