Decision-Making Problem Using Fuzzy TOPSIS Method with Hexagonal Fuzzy Number

2019 ◽  
pp. 421-430
Author(s):  
Naziya Parveen ◽  
P. N. Kamble
Keyword(s):  
Decision Making ◽  
Fuzzy Number ◽  
Fuzzy Topsis ◽  
Topsis Method ◽  
Decision Making Problem

New type of cancer patients based on triangular cubic hesitant fuzzy TOPSIS method

2019 ◽  
Vol 13 (01) ◽  
pp. 2050002
Author(s):  
Aliya Fahmi ◽  
Muhammad Aslam ◽  
Fuad Ali Ahmed Almahdi ◽  
Fazli Amin
Keyword(s):  
Decision Making ◽  
Fuzzy Number ◽  
Hamming Distance ◽  
Fuzzy Topsis ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Topsis Method ◽  
Operational Laws ◽  
New Type ◽  
Mcdm Method

In this paper, we define the new idea of triangular cubic hesitant fuzzy number (TCHFN). We discuss some basic operational laws of triangular cubic hesitant fuzzy number and hamming distance of TCHFNs. We introduce the new concept of triangular cubic hesitant TOPSIS method. Furthermore, we extend the classical cubic hesitant the technique for order of preference by similarity to ideal solution (TOPSIS) method to solve the Multi-Criteria decision-making (MCDM) method based on triangular cubic hesitant TOPSIS method. The new ranking method for TCHFNs is used to rank the alternatives. Finally, an illustrative example is given to verify and demonstrate the practicality and effectiveness of the proposed method.

scholarly journals Trapezoidal Linguistic Cubic Fuzzy TOPSIS Method and Application in a Group Decision Making Program

2019 ◽  
Vol 29 (1) ◽  
pp. 1283-1300 ◽  
Author(s):  
Aliya Fahmi ◽  
Saleem Abdullah ◽  
Fazli Amin ◽  
Muhammad Aslam ◽  
Shah Hussain
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Fuzzy Number ◽  
Fuzzy Numbers ◽  
Group Decision ◽  
Hamming Distance ◽  
Fuzzy Topsis ◽  
Multi Criteria Decision Making ◽  
Topsis Method ◽  
Mcdm Method

Abstract The aim of this paper is to define some new operation laws for the trapezoidal linguistic cubic fuzzy number and Hamming distance. Furthermore, we define and use the trapezoidal linguistic cubic fuzzy TOPSIS method to solve the multi criteria decision making (MCDM) method. The new ranking method for trapezoidal linguistic cubic fuzzy numbers (TrLCFNs) are used to rank the alternatives. Finally, an illustrative example is given to verify and prove the practicality and effectiveness of the proposed method.

Approaches to Multi-Attribute Group Decision-Making Based on Trapezoidal Linguistic Uncertain Cubic Fuzzy TOPSIS Method

2019 ◽  
Vol 15 (02) ◽  
pp. 261-282 ◽  
Author(s):  
Aliya Fahmi ◽  
Fazli Amin ◽  
Saleem Abdullah ◽  
Asad Ali
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Fuzzy Number ◽  
Group Decision ◽  
Hamming Distance ◽  
Fuzzy Topsis ◽  
Ranking Method ◽  
Topsis Method ◽  
Operational Laws ◽  
Mcdm Method

In this paper, we describe the new idea of trapezoidal linguistic uncertain cubic fuzzy number. We discuss some basic operational laws of trapezoidal linguistic uncertain cubic fuzzy number and hamming distance of TrLUCFNs. We introduce the new concept of trapezoidal linguistic uncertain cubic fuzzy TOPSIS method. Furthermore, we extend the classical trapezoidal linguistic uncertain cubic fuzzy TOPSIS method to solve the MCDM method based on trapezoidal linguistic uncertain cubic fuzzy TOPSIS method. The new ranking method for TrLUCFNs is used to rank the alternatives. Finally, an illustrative example is given to verify and prove the practicality and effectiveness of the proposed method.

Linguistic interval-valued Q-rung Orthopair fuzzy TOPSIS method for decision making problem with incomplete weight

2021 ◽  
pp. 1-13
Author(s):  
Muhammad Sajjad Ali Khan ◽  
Amir Sultan Khan ◽  
Israr Ali Khan ◽  
Fawad Hussain ◽  
Wali Khan Mashwani
Keyword(s):  
Decision Making ◽  
Comparative Study ◽  
Fuzzy Set ◽  
Fuzzy Topsis ◽  
Multi Criteria Decision Making ◽  
Topsis Method ◽  
Aggregation Techniques ◽  
Decision Making Problem ◽  
Incomplete Weight ◽  
Interval Valued

The aim of this paper is to introduce the notion of linguistic interval-valued q-rung Orthopair fuzzy set (LIVq-ROFS) as a generalization of linguistic q-rung orthopair fuzzy set. We develop some basic operations, score and accuracy functions to compare the LIVq-ROF values (LIVq-ROFVs). Based on the proposed operations a series of aggregation techniques to aggregate the LIVq-ROFVs and some of their desirable properties are discussed in detail. Moreover, a TOPSIS method is developed to solve a multi-criteria decision making (MCDM) problem under LIVq-ROFS setting. Furthermore, a MCDM approach is proposed based on the developed operators and TOPSIS method, then a practical decision making example is given in order to explain the proposed method. To illustrate to effectiveness and application of the proposed method a comparative study is also conducted.

scholarly journals Evaluation of Investment Opportunities With Interval-Valued Fuzzy Topsis Method

2020 ◽  
Vol 5 (1) ◽  
pp. 461-474 ◽  
Author(s):  
Naiyer Mohammadi Lanbaran ◽  
Ercan Celik ◽  
Muhammed Yiğider
Keyword(s):  
Decision Making ◽  
Fuzzy Set ◽  
Estimation Error ◽  
Fuzzy Topsis ◽  
Optimal Choice ◽  
Topsis Method ◽  
Relative Closeness ◽  
Ideal Solutions ◽  
Fuzzy Logic Theory ◽  
Interval Valued

AbstractThe purpose of this study is extended the TOPSIS method based on interval-valued fuzzy set in decision analysis. After the introduction of TOPSIS method by Hwang and Yoon in 1981, this method has been extensively used in decision-making, rankings also in optimal choice. Due to this fact that uncertainty in decision-making and linguistic variables has been caused to develop some new approaches based on fuzzy-logic theory. Indeed, it is difficult to achieve the numerical measures of the relative importance of attributes and the effects of alternatives on the attributes in some cases. In this paper to reduce the estimation error due to any uncertainty, a method has been developed based on interval-valued fuzzy set. In the suggested TOPSIS method, we use Shannon entropy for weighting the criteria and apply the Euclid distance to calculate the separation measures of each alternative from the positive and negative ideal solutions to determine the relative closeness coefficients. According to the values of the closeness coefficients, the alternatives can be ranked and the most desirable one(s) can be selected in the decision-making process.

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