An improved Taguchi multi-criteria decision-making method based on the hesitant fuzzy correlation coefficient and its application in quality evaluation

2020 ◽  
Author(s):  
Miao Tang ◽  
Tie-Dan Wang ◽  
Ding-Hong Peng
Keyword(s):  
Decision Making ◽  
Correlation Coefficient ◽  
Quality Evaluation ◽  
Multi Criteria Decision Making ◽  
Fuzzy Correlation

Some new Pythagorean fuzzy correlation techniques via statistical viewpoint with applications to decision-making problems

2021 ◽  
pp. 1-13
Author(s):  
Paul Augustine Ejegwa ◽  
Shiping Wen ◽  
Yuming Feng ◽  
Wei Zhang ◽  
Jia Chen
Keyword(s):  
Decision Making ◽  
Correlation Coefficient ◽  
Fuzzy Set ◽  
Intuitionistic Fuzzy Set ◽  
Superior Performance ◽  
Statistical Techniques ◽  
New Techniques ◽  
Reliable Technique ◽  
Performance Indexes ◽  
Fuzzy Correlation

Pythagorean fuzzy set is a reliable technique for soft computing because of its ability to curb indeterminate data when compare to intuitionistic fuzzy set. Among the several measuring tools in Pythagorean fuzzy environment, correlation coefficient is very vital since it has the capacity to measure interdependency and interrelationship between any two arbitrary Pythagorean fuzzy sets (PFSs). In Pythagorean fuzzy correlation coefficient, some techniques of calculating correlation coefficient of PFSs (CCPFSs) via statistical perspective have been proposed, however, with some limitations namely; (i) failure to incorporate all parameters of PFSs which lead to information loss, (ii) imprecise results, and (iii) less performance indexes. Sequel, this paper introduces some new statistical techniques of computing CCPFSs by using Pythagorean fuzzy variance and covariance which resolve the limitations with better performance indexes. The new techniques incorporate the three parameters of PFSs and defined within the range [-1, 1] to show the power of correlation between the PFSs and to indicate whether the PFSs under consideration are negatively or positively related. The validity of the new statistical techniques of computing CCPFSs is tested by considering some numerical examples, wherein the new techniques show superior performance indexes in contrast to the similar existing ones. To demonstrate the applicability of the new statistical techniques of computing CCPFSs, some multi-criteria decision-making problems (MCDM) involving medical diagnosis and pattern recognition problems are determined via the new techniques.

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.

A extended intuitionistic fuzzy Choquet integral correlation coefficient based on Shapley index in multi-criteria decision making

2018 ◽  
Vol 35 (2) ◽  
pp. 2051-2062 ◽  
Author(s):  
Haisheng Zhou ◽  
Guohua Qu ◽  
Yong Zou ◽  
Zengliang Liu ◽  
Chunhua Li ◽  
...  
Keyword(s):  
Decision Making ◽  
Correlation Coefficient ◽  
Choquet Integral ◽  
Multi Criteria Decision Making ◽  
Intuitionistic Fuzzy

scholarly journals Optimizing Material Selection Using a Hybridized Multi-attribute Decision Making Model

2021 ◽  
Vol 16 ◽  
pp. 404-421
Author(s):  
Eslam Mohammed Abdelkader ◽  
Abobakr Al-Sakkaf ◽  
Ghasan Alfalah
Keyword(s):  
Decision Making ◽  
Correlation Coefficient ◽  
Material Selection ◽  
Rank Correlation ◽  
Multi Criteria Decision Making ◽  
Numerical Examples ◽  
Selection Problems ◽  
Spearman’S Rank Correlation ◽  
Spearman’S Rank Correlation Coefficient ◽  
Selection Of

Material selection is a very entangled and decisive stage in the design and development of products. There are large numbers of on hand and newly developed materials available in the market. In addition, inability to select the correct materials adversely affects the reputation and profitability of the company. Thus, designers need to study and trace the performance of available materials with appropriate functionalities. Thus, this research aims at establishing an efficient and systematic platform for the optimum selection of materials while accommodating the designated conflicting performance requirements. The developed model encompasses designing a hybrid decision support system in an attempt to circumvent the shortcomings of single multi-criteria decision making-based (MCDM) models. First, the objective relative importance weights of attributes are interpreted capitalizing on Shannon entropy algorithm. Then, an integrated model that encompasses the utilization of six different types of multi-criteria decision making algorithms is designed to create a reliable selection of material alternatives. The utilized MCDM algorithms comprise weighted product method (WPM), simple additive weighting (SAW), additive ratio assessment (ARAS), new combinative distance-based assessment (CODAS), complex proportional assessment (COPRAS) and technique for order of preference by similarity to ideal solution (TOPSIS). Afterwards, COPELAND algorithm is exploited to generate a consensus and distinct ranking of material alternatives. Eventually, Spearman’s rank correlation analysis is used to evaluate the rankings obtained from the MCDM algorithms. Five numerical examples in diverse fields of material selection are tackled to examine the features and efficiency of the developed integrated model. Results illustrated that the developed model was able to solve the five material selection problems efficiently. On the other hand, no individual MCDM algorithm was able to solve all the assigned material selection problems. For instance, CODAS and TOPSIS only succeeded in solving one and two material selection problems, respectively. It was also inferred that notable differences and perturbations are encountered between the rankings of MCDM algorithms in the first, third, fourth and fifth numerical examples, which necessitates the implementation of COPELAND algorithm. It was also revealed that the highest correlation lied between COPRAS and WPM with an average Spearman’s rank correlation coefficient of 92.67%. On the other hand, the correlation between TOPSIS and CODAS attained the lowest rank with an average Spearman’s rank correlation coefficient of 18.95%. Results also demonstrated that COPRAS accomplished the highest Spearman’s rank correlation coefficient with 59.54%. Hence, it is the most efficient MCDM algorithm among the five algorithms which can serve as a reference for solving material selection problems. It can be also deduced that CODAS and TOPSIS are not advised to be implemented in solving similar material selection problems.

Novel Correlation Coefficient for Intuitionistic Fuzzy Sets and Its Application to Multi-Criteria Decision-Making Problems

2021 ◽  
Vol 10 (2) ◽  
pp. 39-58
Author(s):  
Ejegwa Paul Augustine
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Correlation Coefficient ◽  
Intuitionistic Fuzzy Sets ◽  
Multi Criteria Decision Making ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Generalized Correlation

Correlation coefficient is an essential measuring operator in an intuitionistic fuzzy environment use in solving MCDM problems. In this paper, Xu et al.'s correlation coefficient for IFSs is generalized for an improved output. The objectives of this work are to generalize the triparametric correlation coefficient for IFSs proposed by Xu et al. and unravel its applicability in some MCDM problems. The generalized correlation coefficient for IFSs is characterized with some number of results. Some numerical illustrations are supplied to validate the preeminence of the generalized correlation coefficient for IFSs over some existing correlation coefficient measures. In addition, some MCDM problems such as determination of suitable lecturer for course allocation and personnel promotion exercise captured in intuitionistic fuzzy pairs are discussed with the aid of the proposed correlation coefficient.

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