Some Aggregation Operators and Novel Approaches for Decision Making under Several Fuzzy Environments

Abstract

The primary objective of this thesis is to present aggregation operators and model formulation for solving decision-making problems that consist of multiple attributes under inexact environments. The Multiple Attribute Decision Making (MADM) technique has been extensively applied in various real-life problems like site selection, economic evaluation, clustering analysis, business studies, medical diagnosis, and computer science. However, in reality, information is often imprecise due to various factors. Therefore, in MADM, the information about each alternative concerning the attributes needs to be evaluated in several fuzzy set extensions, which can be appropriately adapted to account for imprecise information. This thesis develops several decision-making techniques with unknown attribute weights under different fuzzy environments. The validity and applicability of all the proposed models are then illustrated by solving various real-world problems throughout the dissertation. newline