[1]XU Yingying,ZOU Li,HUANG Zhixin,et al.Linguistic truth-valued intuitionistic fuzzy multi-attribute decision making based on TOPSIS[J].CAAI Transactions on Intelligent Systems,2017,12(4):504-510.[doi:10.11992/tis.201608008]
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CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
12
Number of periods:
2017 4
Page number:
504-510
Column:
学术论文—自然语言处理与理解
Public date:
2017-08-25
- Title:
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Linguistic truth-valued intuitionistic fuzzy multi-attribute decision making based on TOPSIS
- Author(s):
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XU Yingying1; ZOU Li1; HUANG Zhixin2; PAN Chang1
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1. School of Computer and Information Technology, Liaoning Normal University, Dalian 116081, China;
2. School of Mathematics, Liaoning Normal University, Dalian 116081, China
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- Keywords:
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TOPSIS; linguistic truth-valued intuitionistic fuzzy pairs; normalized distance; ideal point; multi-attribute decision making
- CLC:
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TP181
- DOI:
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10.11992/tis.201608008
- Abstract:
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For multi-attribute decision making problems with fuzzy linguistic-valued information, in this paper, we propose a linguistic truth-valued intuitionistic fuzzy multi-attribute decision making approach based on the technique for order performance by similarity to ideal solution (TOPSIS), in combination with the traditional TOPSIS approach. On the basis of linguistic truth-valued intuitionistic fuzzy algebra, in our approach, we used linguistic truth-valued intuitionistic fuzzy pairs to express fuzzy linguistic-valued information that is both comparable and incomparable. We define the normalized distance algorithm for linguistic truth-valued intuitionistic fuzzy pairs and discuss its related properties. We propose linguistic truth-valued intuitionistic fuzzy positive and negative ideal points by calculating the distances between the attribute values of every scheme with positive and negative ideal points to obtain their relative degree of closeness. From the ranking result of the relative degree of closeness, we can determine the best scheme. We give an example to illustrate the reasonability and effectiveness of our proposed decision-making approach.