<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The 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.</p></abstract>

The 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.

Evaluation of Investment Opportunities With Interval-Valued Fuzzy Topsis Method

naiyer.mohammadi.lanbaran13@ogr.atauni.edu.tr

Department of Mathematics, Faculty of Science

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Evaluation of Investment Opportunities With Interval-Valued Fuzzy Topsis Method"}

The 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...

Evaluation of Investment Opportunities With Interval-Valued Fuzzy Topsis Method

Published Online: Apr 10, 2020

Page range: 461 - 474

Received: May 29, 2019

Accepted: Jun 13, 2019

DOI: https://doi.org/10.2478/amns.2020.1.00044

Keywords
Multi-Criteria decision making, Fuzzy logic theory, Interval-Valued Fuzzy TOPSIS Analysis, Euclid distance, Shannon Entropy

© 2020 Naiyer Mohammadi Lanbaran et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Evaluation of Investment Opportunities With Interval-Valued Fuzzy Topsis Method

Published Online: Apr 10, 2020

Page range: 461 - 474

Received: May 29, 2019

Accepted: Jun 13, 2019

DOI: https://doi.org/10.2478/amns.2020.1.00044

KeywordsMulti-Criteria decision making, Fuzzy logic theory, Interval-Valued Fuzzy TOPSIS Analysis, Euclid distance, Shannon Entropy

© 2020 Naiyer Mohammadi Lanbaran et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Multi-Criteria decision making, Fuzzy logic theory, Interval-Valued Fuzzy TOPSIS Analysis, Euclid distance, Shannon Entropy