Analysis of trends and applications of Multi-Criteria Decision-Making methods

Abstract

Multi-Criteria Decision-Making (MCDM) methods provide effective tools for evaluating, comparing, and ranking alternatives based on multiple criteria, thereby assisting decision-makers in making rational and well-founded choices. This study aims to categorize MCDM methods and explore the practical contexts in which they are applied by mining data from the keywords and abstracts of 14,089 scientific research articles in the Scopus database using text mining techniques. In recent years, MCDM research has grown significantly, driven by contributions from Asia and Europe and spanning diverse fields like computer science, engineering, and mathematics. Supported by substantial funding, these studies highlight MCDM’s broad applicability and enduring impact on decision-making. The analysis reveals the diversity of methods such as the Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), and fuzzy variants are identified as central methods with application contexts ranging from supply chain management and performance evaluation to energy and environmental management, among others. Moreover, sensitivity analysis is frequently applied due to its critical role in enhancing the reliability of MCDM methods, ensuring that small changes in input parameters do not significantly impact the final decision outcomes. Additional findings, including specific applications and methodological trends, will be further discussed in the discussion section. These findings provide a comprehensive overview of the prevalence and usage trends of MCDM methods, while also highlighting research gaps and potential future applications.