Interval-valued Intuitionistic Fuzzy TOPSIS method for Supplier Selection Problem

Author(s):  
Ashutosh Tiwari ◽  
Q.M. Danish Lohani ◽  
Pranab K. Muhuri
2021 ◽  
Vol 27 (1) ◽  
pp. 24-52
Author(s):  
Cengiz Kahraman ◽  
◽  
Nurşah Alkan ◽  

The membership function of a general type-2 fuzzy set is three-dimensional in order to incorporate its vagueness through the third dimension. Similarly, Circular intuitionistic fuzzy sets (CIFSs) have been recently introduced by Atanassov (2020) as a new extension of intuitionistic fuzzy sets, which are represented by a circle representing the vagueness of the membership function. CIFSs allow decision-makers to express their judgments including this vagueness. In this study, the TOPSIS method, which is one of the most used multi-criteria decision-making methods is extended to its CIF version. The proposed CIF-TOPSIS methodology is applied to the supplier selection problem. Then, a sensitivity analysis based on criteria weights is conducted to check the robustness of the proposed approach. A comparative analysis with single-valued intuitionistic fuzzy TOPSIS method is also performed to verify the developed approach and to demonstrate its effectiveness


2019 ◽  
Vol 50 ◽  
pp. 9-24 ◽  
Author(s):  
Ashkan Memari ◽  
Ahmad Dargi ◽  
Mohammad Reza Akbari Jokar ◽  
Robiah Ahmad ◽  
Abd. Rahman Abdul Rahim

2021 ◽  
Vol 13 (2) ◽  
pp. 985
Author(s):  
Adem Pınar ◽  
Rouyendegh Babak Daneshvar ◽  
Yavuz Selim Özdemir

Supply chain management is to improve competitive stress. In today’s world, competitive terms and customer sense have altered in favor of an environmentalist manner. As a result of this, green supplier selection has become a very important topic. In the green supplier selection approach, agility, lean process, sustainability, environmental sensitivity, and durability are pointed. Like the classical supplier selection problems, environmental criteria generally emphasize green supplier selection. However, these two problem approaches are different from each other in terms of carbon footprint, water consumption, environmental and recycling applications. Due to the problem structure, a resolution is defined that includes an algorithm based on q-Rung Orthopair Fuzzy (q-ROF) TOPSIS method. Brief information about q-ROF sets is given before the methodology of the q-ROF model is introduced. By using the proposed method and q-ROF sets, an application was made with today’s uncertain conditions. In the conclusion part, a comparison is made with classical TOPSIS, Intuitionistic Fuzzy TOPSIS and q-ROF TOPSIS methodology. As a result, more stable and accurate results are obtained with q-ROF TOPSIS.


2021 ◽  
pp. 1-14
Author(s):  
Ahmet Sarucan ◽  
Mehmet Emin Baysal ◽  
Orhan Engin

The membership functions of the intuitionistic fuzzy sets, Pythagorean fuzzy sets, neutrosophic sets and spherical fuzzy sets are based on three dimensions. The aim is to collect the expert’s judgments. Physicians serve patients in the physician selection problem. It is difficult to measure the service’s quality due to the variability in patients’ preferences. The patients physician preference criteria is differing and uncertainties. Thus, solving this problem with fuzzy method is more appropriate. In this study, we considered the physician selection as a multi-criteria decision-making problem. Solving this problem, we proposed a spherical fuzzy TOPSIS method. We used the five alternatives and eight criteria. The application was performed in the neurology clinics of Konya city state hospitals. In addition, we solved the same problem by the intuitionistic fuzzy TOPSIS method. We compared the solutions of two methods with each other. We found that the spherical fuzzy TOPSIS method is effective for solving the physician selection problem.


2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Zhi-yong Bai

This paper proposes an improved score function for the effective ranking order of interval-valued intuitionistic fuzzy sets (IVIFSs) and an interval-valued intuitionistic fuzzy TOPSIS method based on the score function to solve multicriteria decision-making problems in which all the preference information provided by decision-makers is expressed as interval-valued intuitionistic fuzzy decision matrices where each of the elements is characterized by IVIFS value and the information about criterion weights is known. We apply the proposed score function 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. Finally, two illustrative examples for multicriteria fuzzy decision-making problems of alternatives are used as a demonstration of the applications and the effectiveness of the proposed decision-making method.


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