A Novel Method for Determining the Attribute Weights in the Multiple Attribute Decision-Making with Neutrosophic Information through Maximizing the Generalized Single-Valued Neutrosophic Deviation

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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Fuzzy Attribute Expansion Method for Multiple Attribute Decision-Making with Partial Attribute Values and Weights Unknown and Its Applications

Symmetry ◽

10.3390/sym10120717 ◽

2018 ◽

Vol 10 (12) ◽

pp. 717

Author(s):

Jinbao Zhuo ◽

Weifeng Shi ◽

Ying Lan

Keyword(s):

Decision Making ◽

Spline Interpolation ◽

Expansion Method ◽

Fuzzy Comprehensive Evaluation ◽

Multiple Attribute Decision Making ◽

Clustering Methods ◽

Attribute Weights ◽

Multiple Attribute ◽

Interpolation Technique ◽

Spline Interpolation Technique

In the real world, there commonly exists types of multiple attribute decision-making (MADM) problems with partial attribute values and weights totally unknown. Symmetry among some attribute information that is already known and unknown, and symmetry between the pure attribute set and fuzzy attribute membership set can be a considerable way to solve this type of MADM problem. In this paper, a fuzzy attribute expansion method is proposed to solve this type of problem based on two key techniques: the spline interpolation technique and the attribute weight reconfiguration technique, which are respectively used for the determination of attribute values and the reconfiguration of attribute weights. The spline interpolation technique to expand attribute values can enhance the performance of some regression methods and clustering methods by the comparisons between the results of these methods dealing with practical cases with and without the application of the technique, which further illustrates the effectiveness of this technique. For MADM problems with partial attribute values and weights totally unknown, compared with traditional fuzzy comprehensive evaluation (FCE), FCE with the application of fuzzy attribute expansion method can obtain results more similar with the ones when all attribute values and weights are known, which is proved by the practical power quality evaluation example.

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Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making

Mathematical and Computer Modelling ◽

10.1016/j.mcm.2009.07.016 ◽

2010 ◽

Vol 51 (1-2) ◽

pp. 1-12 ◽

Cited By ~ 167

Author(s):

Ying-Ming Wang ◽

Ying Luo

Keyword(s):

Decision Making ◽

Multiple Attribute Decision Making ◽

Attribute Weights ◽

Multiple Attribute ◽

Standard Deviations

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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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Multiple Attribute Decision Making Based on Cross-Evaluation with Uncertain Decision Parameters

Mathematical Problems in Engineering ◽

10.1155/2016/4313247 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Tao Ding ◽

Liang Liang ◽

Min Yang ◽

Huaqing Wu

Keyword(s):

Decision Making ◽

Multiple Attribute Decision Making ◽

Service Companies ◽

Attribute Weights ◽

Research Fields ◽

Multiple Attribute ◽

Single Attribute ◽

The Stability ◽

The City

Multiple attribute decision making (MADM) problem is one of the most common and popular research fields in the theory of decision science. A variety of methods have been proposed to deal with such problems. Nevertheless, many of them assumed that attribute weights are determined by different types of additional preference information which will result in subjective decision making. In order to solve such problems, in this paper, we propose a novel MADM approach based on cross-evaluation with uncertain parameters. Specifically, the proposed approach assumes that all attribute weights are uncertain. It can overcome the drawback in prior research that the alternatives’ ranking may be determined by a single attribute with an overestimated weight. In addition, the proposed method can also balance the mean and deviation of each alternative’s cross-evaluation score to guarantee the stability of evaluation. Then, this method is extended to a more generalized situation where the attribute values are also uncertain. Finally, we illustrate the applicability of the proposed method by revisiting two reported studies and by a case study on the selection of community service companies in the city of Hefei in China.

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Extending ARAS with Integration of Objective Attribute Weighting under Spherical Fuzzy Environment

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622021500267 ◽

2021 ◽

pp. 1-26

Author(s):

Sait Gül

Keyword(s):

Decision Making ◽

Multiple Attribute Decision Making ◽

Decision Makers ◽

Owa Operator ◽

Attribute Weighting ◽

Preference Domain ◽

Attribute Weights ◽

Fuzzy Environment ◽

Multiple Attribute ◽

Collection Time

Various fuzzy sets have been developed in the recent years to model the uncertainty in judgments. Spherical fuzzy set (SFS) concept is one of these developments. It can provide an extensive preference domain for decision-makers by allowing them to state their hesitancy more explicitly. The peculiarity of SFS is that the squared sum of membership, nonmembership, and hesitancy degrees should be between 0 and 1 while each is independently defined in [0, 1]. In this study, ARAS as one of the most applied multiple attribute decision-making approaches is extended into a spherical fuzzy environment. Entropy-based and OWA operator-based objective attribute weights are also integrated with the newly proposed spherical fuzzy ARAS for coping with the drawbacks of subjective weighting such as longer data collection time and manipulation risk. The applicability of the proposition is shown in a hypothetical example of a product design problem and its robustness is shown by a comparative analysis.

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