Exploring decision-making models and their applications: A comparative analysis

Abstract

Decision-making is an essential part of solving complex problems that are faced in different fields such as business, healthcare, logistics, and government administration. As challenges become more complex, the need for logical and systematic decision-making models has resulted in the formulation of a wide variety of models to achieve optimal results under multiple constraints and conflicting goals. This research investigates some of the leading decision-making methods like the Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Decision Tree Analysis, and Multi-Attribute Utility Theory (MAUT). The models are compared based on their efficiency, complexity, and applicability in varying application areas. The actual use of each method is demonstrated with a real-world example case of vendor selection. These comparative results are illustrated in tables as well as graphical figures. The discussion highlights that there is no single approach to be used in all contexts, but instead, the choice of an effective model should rely on the type of problem, the nature of data available, priorities for stakeholders, and the complexity of the decision situation.