Study of Multi Criteria Decision Making Algorithm Under Fuzzy Environment

Abstract

Real-world decision-making often involves uncertainty, imprecise data, and varying opinions newlinefrom multiple experts especially in multi-attribute and group decision-making problems. Traditional newlinemethods frequently fall short in handling this complexity. This research focuses on newlineaddressing these challenges by developing new techniques within the framework of type-2 newlinefuzzy set theory, which is known for its ability to better model hesitation and ambiguity. newlineThe primary objective of this research is to develop methods for solving multi-attribute newlinedecision-making (MADM) and multi-attribute group decision-making (MAGDM) problems newlineunder uncertain conditions. The study introduces a range of innovative operators and information newlinemeasures, such as prioritized weighted models, aggregation operators, geometric operators, newlineharmonic operators, Bonferroni-based operators, and Maclaurin symmetric means, within newlinethe framework of type-2 fuzzy sets and their interval and intuitionistic extensions. Closeness newlinecoefficient ranking method applies on type-2 intuitionistic fuzzy multi-criteria group decision newlinemaking situations. newlineThe proposed methods are applied to real-world decision-making scenarios such as talent newlineselection and medical diagnosis to demonstrate their practical value and improved performance. newlineThrough these applications, the thesis shows that Type-2 fuzzy models, particularly newlinetheir interval-valued and intuitionistic extensions, offer a more flexible and reliable approach newlinefor managing uncertainty in decision-making processes. newline