Novel method for hybrid multiple attribute decision making based on TODIM method

In this paper, a new multiple attribute decision-making (MADM) method under q-rung dual hesitant fuzzy environment from the perspective of aggregation operators is proposed. First, some aggregation operators are proposed for fusing q-rung dual hesitant fuzzy sets (q-RDHFSs). Afterwards, we present properties and some desirable special cases of the new operators. Second, a new entropy measure for q-RDHFSs is developed, which defines a method to calculate the weight information of aggregated q-rung dual hesitant fuzzy elements. Third, a novel MADM method is introduced to deal with decision-making problems under q-RDHFSs environment, wherein weight information is completely unknown. Finally, we present numerical example to show the effectiveness and performance of the new method. Additionally, comparative analysis is conducted to prove the superiorities of our new MADM method. This study mainly contributes to a novel method, which can help decision makes select optimal alternatives when dealing with practical MADM problems.

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TODIM method for interval-valued Pythagorean fuzzy multiple attribute decision making

International Journal of Knowledge-based and Intelligent Engineering Systems ◽

10.3233/kes-180389 ◽

2018 ◽

Vol 22 (4) ◽

pp. 249-259 ◽

Cited By ~ 3

Author(s):

Yu-Han Huang ◽

Gui-Wu Wei

Keyword(s):

Decision Making ◽

Multiple Attribute Decision Making ◽

Multiple Attribute ◽

Todim Method ◽

Interval Valued

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Finding Missing Links in Complex Networks: A Multiple-Attribute Decision-Making Method

Complexity ◽

10.1155/2018/3579758 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 3

Author(s):

Longjie Li ◽

Shenshen Bai ◽

Mingwei Leng ◽

Lu Wang ◽

Xiaoyun Chen

Keyword(s):

Decision Making ◽

Complex Networks ◽

Similarity Measure ◽

Real World ◽

Link Prediction ◽

State Of The Art ◽

Multiple Attribute Decision Making ◽

Ideal Solution ◽

Multiple Attribute ◽

Novel Method

Link prediction, which aims to forecast potential or missing links in a complex network based on currently observed information, has drawn growing attention from researchers. To date, a host of similarity-based methods have been put forward. Usually, one method harbors the idea that one similarity measure is applicable to various networks, and thus has performance fluctuation on different networks. In this paper, we propose a novel method to solve this issue by regarding link prediction as a multiple-attribute decision-making (MADM) problem. In the proposed method, we consider RA, LP, and CAR indices as the multiattribute for node pairs. The technique for order performance by similarity to ideal solution (TOPSIS) is adopted to aggregate the multiattribute and rank node pairs. The proposed method is not limited to only one similarity measure, but takes separate measures into account, since different networks may have different topological structures. Experimental results on 10 real-world networks manifest that the proposed method is superior in comparison to state-of-the-art methods.

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