Abstract
A wide variety of human decision-making is based on double-sided or bipolar judgmental thinking on a positive side and a negative side. This paper develops a new method called bipolar fuzzy extended TOPSIS based on entropy weights to address the multi-criteria decision-making problems involving bipolar measurements with positive and negative values. The extended bipolar fuzzy TOPSIS method incorporates the capability of bipolar information into the TOPSIS to address the interactions between criteria and measure the aggregate values on a bipolar scale. In practical problems, this method can be used to measure the benefits and side effects of medical treatments. We also discuss some novel applications of bipolar fuzzy competition graphs in food webs and present certain algorithms to compute the strength of competition between species.
Highlights
Multi-criteria decision-making (MCDM) models have been developed and implemented in various fields such as engineering, economics, management, business and information technology
We present the bipolar fuzzy extended TOPSIS multi-criteria decision-making model based on entropy weights for the selection of teeth replacement options with minimum side effects and maximum benefits
A bipolar fuzzy set is an extension of a fuzzy set with the additional possibility to represent bipolar uncertainty and vagueness that exist in real-world situations
Summary
Multi-criteria decision-making (MCDM) models have been developed and implemented in various fields such as engineering, economics, management, business and information technology. TOPSIS is the most implemented technique for decision-making problems, especially in medical science, but due to its limitations in dealing with bipolar uncertainty, which is in the perception of decision-makers and in the given information, it does not give accurate results. It is a very good and rational approach which gives computational efficiency in a simple mathematical form. In the existing bipolar fuzzy TOPSIS methods, weights to alternatives are chosen arbitrarily according to the choice of decision-makers We have extended this technique using entropy weights. For other terminologies and applications that are not mentioned in this paper, readers may refer to [28,29,30,31,32,33]
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