A Multi-attribute Decision-Making Approach Based on Spherical Fuzzy Sets for Yunnan Baiyao’s R&D Project Selection Problem

2019 ◽  
Vol 21 (7) ◽  
pp. 2168-2191 ◽  
Author(s):  
Peide Liu ◽  
Baoying Zhu ◽  
Peng Wang
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Project Selection ◽  
Selection Problem ◽  
Multi Attribute Decision Making

scholarly journals TOPSIS Method Based on Complex Spherical Fuzzy Sets with Bonferroni Mean Operators

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1739
Author(s):  
Zeeshan Ali ◽  
Tahir Mahmood ◽  
Miin-Shen Yang
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Unit Interval ◽  
Polar Coordinates ◽  
Topsis Method ◽  
Bonferroni Mean ◽  
Operational Laws ◽  
Special Cases ◽  
Order Preference ◽  
Multi Attribute Decision Making

The theory of complex spherical fuzzy sets (CSFSs) is a mixture of two theories, i.e., complex fuzzy sets (CFSs) and spherical fuzzy sets (SFSs), to cope with uncertain and unreliable information in realistic decision-making situations. CSFSs contain three grades in the form of polar coordinates, e.g., truth, abstinence, and falsity, belonging to a unit disc in a complex plane, with a condition that the sum of squares of the real part of the truth, abstinence, and falsity grades is not exceeded by a unit interval. In this paper, we first consider some properties and their operational laws of CSFSs. Additionally, based on CSFSs, the complex spherical fuzzy Bonferroni mean (CSFBM) and complex spherical fuzzy weighted Bonferroni mean (CSFWBM) operators are proposed. The special cases of the proposed operators are also discussed. A multi-attribute decision making (MADM) problem was chosen to be resolved based on the proposed CSFBM and CSFWBM operators. We then propose the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method based on CSFSs (CSFS-TOPSIS). An application example is given to delineate the proposed methods and a close examination is undertaken. The advantages and comparative analysis of the proposed approaches are also presented.

scholarly journals A Multi-Attribute Pearson’s Picture Fuzzy Correlation-Based Decision-Making Method

Mathematics ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 999 ◽  
Author(s):  
Jin ◽  
Wu ◽  
Sun ◽  
Zeng ◽  
Luo ◽  
...  
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Correlation Coefficient ◽  
Ideal Point ◽  
New Correlation ◽  
Fuzzy Correlation ◽  
Relative Proximity ◽  
Fuzzy Madm ◽  
Multi Attribute Decision Making ◽  
Picture Fuzzy Sets

As a generalization of several fuzzy tools, picture fuzzy sets (PFSs) hold a special ability to perfectly portray inherent uncertain and vague decision preferences. The intention of this paper is to present a Pearson’s picture fuzzy correlation-based model for multi-attribute decision-making (MADM) analysis. To this end, we develop a new correlation coefficient for picture fuzzy sets, based on which a Pearson’s picture fuzzy closeness index is introduced to simultaneously calculate the relative proximity to the positive ideal point and the relative distance from the negative ideal point. On the basis of the presented concepts, a Pearson’s correlation-based model is further presented to address picture fuzzy MADM problems. Finally, an illustrative example is provided to examine the usefulness and feasibility of the proposed methodology.

Cubic Pythagorean fuzzy sets and their application to multi-attribute decision making with unknown weight information

2019 ◽  
Vol 37 (1) ◽  
pp. 1529-1544 ◽  
Author(s):  
Syed Zaheer Abbas ◽  
Muhammad Sajjad Ali Khan ◽  
Saleem Abdullah ◽  
Huafei Sun ◽  
Fawad Hussain
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Pythagorean Fuzzy Sets ◽  
Multi Attribute Decision Making

scholarly journals SOLVING CONSTRUCTION PROJECT SELECTION PROBLEM BY A NEW UNCERTAIN WEIGHTING AND RANKING BASED ON COMPROMISE SOLUTION WITH LINEAR ASSIGNMENT APPROACH

2019 ◽  
Vol 25 (3) ◽  
pp. 241-251 ◽  
Author(s):  
Reza Davoudabadi ◽  
Seyed Meysam Mousavi ◽  
Jonas Šaparauskas ◽  
Hossein Gitinavard
Keyword(s):  
Fuzzy Sets ◽  
Alternative Assessment ◽  
Construction Project ◽  
Project Selection ◽  
Selection Problem ◽  
Uncertain Information ◽  
Compromise Solution ◽  
Additional Information ◽  
Criteria Weights ◽  
Linear Assignment

Selecting a suitable construction project is a significant issue for contractors to decrease their costs. In real cases, the imprecise and uncertain information lead to decisions made based on vagueness. Fuzzy sets theory could help decision makers (DMs) to address incomplete information. However, this article develops a new integrated multi-criteria group decision-making model based on compromise solution and linear assignment approaches with interval-valued intuitionistic fuzzy sets (IVIFSs). IVIFSs by presenting a membership and non-membership degree for each candidate based on appraisement criteria could decrease the vagueness of selection decisions. The proposed algorithm involves a new decision process under uncertain conditions to determine the importance of criteria and DMs, separately. In this regard, no subjective or additional information is needed for this process; only the input information required is an alternative assessment matric. In this approach, weights of criteria and DMs are specified based on novel indexes to increase the reliability of obtained results. In this respect, the criteria’ weights are computed regarding entropy concepts. The basis for calculating the weight of each DM is the distance between each DM and an average of the DMs’ community. Furthermore, the linear assignment model is extended to rank the candidates. A case study about the construction project selection problem (CPSP) is illustrated to indicate the application of proposed model.

scholarly journals Decision-Making Approach under Pythagorean Fuzzy Yager Weighted Operators

Mathematics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 70 ◽  
Author(s):  
Gulfam Shahzadi ◽  
Muhammad Akram ◽  
Ahmad N. Al-Kenani
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Aggregation Operators ◽  
Weighted Averaging ◽  
Ordered Weighted Averaging ◽  
Multi Attribute Decision Making ◽  
Parametric Families

In fuzzy set theory, t-norms and t-conorms are fundamental binary operators. Yager proposed respective parametric families of both t-norms and t-conorms. In this paper, we apply these operators for the analysis of Pythagorean fuzzy sets. For this purpose, we introduce six families of aggregation operators named Pythagorean fuzzy Yager weighted averaging aggregation, Pythagorean fuzzy Yager ordered weighted averaging aggregation, Pythagorean fuzzy Yager hybrid weighted averaging aggregation, Pythagorean fuzzy Yager weighted geometric aggregation, Pythagorean fuzzy Yager ordered weighted geometric aggregation and Pythagorean fuzzy Yager hybrid weighted geometric aggregation. These tools inherit the operational advantages of the Yager parametric families. They enable us to study two multi-attribute decision-making problems. Ultimately we can choose the best option by comparison of the aggregate outputs through score values. We show this procedure with two practical fully developed examples.

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