A multi-attribute decision making method based on evidence theory and average operator

Ya Li; Gang Shu; Xinyang Deng; Yong Deng

doi:10.12733/jcis8924

A multi-attribute decision making method based on evidence theory and average operator

Ya Li, Gang Shu, Xinyang Deng, Yong Deng

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

Aggregating decision-maker's evaluation and acquiring a ranking of alternatives is very important to multiple attribute decision making (MADM). In this paper, we propose a new method to solve MADM problems in which all the information provided by the decision-makers is presented as interval-valued intuitionistic fuzzy numbers (IVIFNs). In the method, IVIFNs are converted into a group of basic probability assignment (BPA) by continuous interval argument ordered weighted average (C-OWA) operator. And then evidence theory is applied to aggregate BPAs into a comprehensive BPA. Based on this single overall BPA, a ranking order of candidates can be obtained. Finally, a numerical example is used to illustrate the effectiveness of the proposed method.

Original language	English
Pages (from-to)	595-601
Number of pages	7
Journal	Journal of Computational Information Systems
Volume	10
Issue number	2
DOIs	https://doi.org/10.12733/jcis8924
State	Published - 15 Jan 2014
Externally published	Yes

Keywords

Continuous interval argument ordered weighted average (C-OWA) operator
Evidence theory
Interval-valued intuitionistic fuzzy number (IVIFN)
Multiple attribute decision making (MADM)

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10.12733/jcis8924

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abstract = "Aggregating decision-maker's evaluation and acquiring a ranking of alternatives is very important to multiple attribute decision making (MADM). In this paper, we propose a new method to solve MADM problems in which all the information provided by the decision-makers is presented as interval-valued intuitionistic fuzzy numbers (IVIFNs). In the method, IVIFNs are converted into a group of basic probability assignment (BPA) by continuous interval argument ordered weighted average (C-OWA) operator. And then evidence theory is applied to aggregate BPAs into a comprehensive BPA. Based on this single overall BPA, a ranking order of candidates can be obtained. Finally, a numerical example is used to illustrate the effectiveness of the proposed method.",

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AU - Shu, Gang

AU - Deng, Xinyang

AU - Deng, Yong

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AB - Aggregating decision-maker's evaluation and acquiring a ranking of alternatives is very important to multiple attribute decision making (MADM). In this paper, we propose a new method to solve MADM problems in which all the information provided by the decision-makers is presented as interval-valued intuitionistic fuzzy numbers (IVIFNs). In the method, IVIFNs are converted into a group of basic probability assignment (BPA) by continuous interval argument ordered weighted average (C-OWA) operator. And then evidence theory is applied to aggregate BPAs into a comprehensive BPA. Based on this single overall BPA, a ranking order of candidates can be obtained. Finally, a numerical example is used to illustrate the effectiveness of the proposed method.

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A multi-attribute decision making method based on evidence theory and average operator

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Keywords

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