An uncertain multiple attribute decision making method based on the ideal point with four-point interval numbers

2015 ◽  
pp. 457-462
Keyword(s):  
Decision Making ◽  
Ideal Point ◽  
Multiple Attribute Decision Making ◽  
Interval Numbers ◽  
Multiple Attribute ◽  
The Ideal

scholarly journals DENSITY AGGREGATION OPERATORS BASED ON THE INTUITIONISTIC TRAPEZOIDAL FUZZY NUMBERS FOR MULTIPLE ATTRIBUTE DECISION MAKING

2014 ◽  
Vol 19 (Supplement_1) ◽  
pp. S454-S470 ◽  
Author(s):  
Peide Liu ◽  
Xiaocun Yu
Keyword(s):  
Decision Making ◽  
Fuzzy Numbers ◽  
Multiple Attribute Decision Making ◽  
Expected Value ◽  
Aggregation Operators ◽  
Ranking Method ◽  
Interval Numbers ◽  
Trapezoidal Fuzzy Numbers ◽  
Multiple Attribute

With respect to the multiple attribute decision making problems in which the attribute values take the form of the intuitionistic trapezoidal fuzzy numbers, some methods based on density aggregation operators are proposed. Firstly, the definition, expected value and the ranking method of intuitionistic trapezoidal fuzzy numbers are introduced, and the method of calculating density weighted vector is proposed. Then some density aggregation operators based on interval numbers and intuitionistic trapezoidal fuzzy numbers are developed, and a multiple attribute decision making method is presented. Finally an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.

PROJECTION MODELS FOR INTUITIONISTIC FUZZY MULTIPLE ATTRIBUTE DECISION MAKING

2010 ◽  
Vol 09 (02) ◽  
pp. 267-280 ◽  
Author(s):  
ZESHUI XU ◽  
HUI HU
Keyword(s):  
Decision Making ◽  
Fuzzy Numbers ◽  
Ideal Point ◽  
Multiple Attribute Decision Making ◽  
Included Angle ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy ◽  
Multiple Attribute ◽  
Fuzzy Ideal ◽  
Interval Valued

The aim of this paper is to investigate the intuitionistic fuzzy multiple attribute decision-making problems where the attribute values are expressed in intuitionistic fuzzy numbers or interval-valued intuitionistic fuzzy numbers. We introduce some notions, such as intuitionistic fuzzy ideal point, interval-valued intuitionistic fuzzy ideal point, the modules of intuitionistic fuzzy numbers, and interval-valued intuitionistic fuzzy numbers. We also introduce the cosine of the included angle between the attribute value vectors of each alternative and the intuitionistic fuzzy ideal point, and the cosine of the included angle between the attribute value vectors of each alternative and the interval-valued intuitionistic fuzzy ideal point. Then we establish two projection models to measure the similarity degrees between each alternative and the intuitionistic fuzzy ideal point, and between each alternative and the interval-valued intuitionistic fuzzy ideal point. Based on the projection models, we can rank the given alternatives and then select the most desirable one. Finally, we illustrate the developed projection models with a numerical example.

Extension of TOPSIS for Multiple Attribute Decision Making Using Intuitionistic Fuzzy Sets

2012 ◽  
Vol 433-440 ◽  
pp. 4053-4058 ◽  
Author(s):  
Yuan Yuan ◽  
Li Yang He
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Quantitative Estimation ◽  
Ideal Point ◽  
Real Life ◽  
Multiple Attribute Decision Making ◽  
Intuitionistic Fuzzy Sets ◽  
Distance Measures ◽  
Intuitionistic Fuzzy ◽  
Multiple Attribute

This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document. Due to the nature of vagueness inherent to real-life situations, some fuzzy data are deemed to suitable enough to describe the qualitative and/or quantitative estimation for decision making problems. Therefore, a new method for multiple attribute decision making under fuzzy environment is discussed, in which the attribute values take the form of intuitionistic fuzzy numbers. To overcome some disadvantages of existing distance measures like indiscrimination, counterintuitive results and difficulty of interpretation, we introduce a new class of distance for describing the deviation degrees between intuitionistic fuzzy sets. Furthermore, the measure of similarity degree for each alternative to ideal point is calculated through using the new proposed fuzzy distance. A model of TOPSIS is designed with the introduction of the particular closeness coefficient composed of similarity degrees. Then, we extend the TOPSIS method to aggregate the fuzzy information corresponding to each alternative, and rank the alternatives according to their closeness coefficients. Finally, an illustrative example is given to demonstrate the proposed approach practicality and effectiveness.

Correlation Coefficients of Refined-Single Valued Neutrosophic Sets and Their Applications in Multiple Attribute Decision-Making

2019 ◽  
Vol 23 (3) ◽  
pp. 421-426 ◽  
Author(s):  
Changxing Fan ◽  
Keyword(s):  
Decision Making ◽  
Correlation Coefficient ◽  
Correlation Coefficients ◽  
Multiple Attribute Decision Making ◽  
Neutrosophic Sets ◽  
Actual Application ◽  
Multiple Attribute ◽  
Weighted Correlation ◽  
The Ideal

The paper presents the correlation coefficient of refined-single valued neutrosophic sets (Refined-SVNSs) based on the extension of the correlation of single valued neutrosophic sets (SVNSs), and then a decision making method is proposed by the use of the weighted correlation coefficient of Refined-SVNSs. Through the weighted correlation coefficient between the ideal alternative and each alternative, we can rank all alternatives and the best one of all alternatives can be easily identified as well. Finally, to prove this decision making method proposed in this paper is useful to deal with the actual application, we use an example to illustrate it.

An Approach to Multiple Attribute Decision Making with Triangular Linguistic Information

2016 ◽  
Vol 13 (10) ◽  
pp. 7289-7292
Author(s):  
Rong-Fang Chen
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Project Risk ◽  
Linguistic Information ◽  
Bonferroni Mean ◽  
Aggregation Techniques ◽  
Multiple Attribute ◽  
Special Cases ◽  
The Ideal

In this paper, we investigate the multiple attribute decision making problems with triangular linguistic information. Motivated by the ideal of Bonferroni mean, we develop the aggregation techniques called the triangular linguistic Bonferroni mean (TLBM) operator for aggregating the triangular linguistic information. We study its properties and discuss its special cases. For the situations where the input arguments have different importance, we then define the triangular linguistic weighted Bonferroni mean (TLWBM) operator, based on which we develop the procedure for multiple attribute decision making under the triangular linguistic environments. Finally, a practical example for evaluating the engineer project risk is given to verify the developed approach and to demonstrate its practicality and effectiveness.

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