A Comprehensive Review of MCDM Methods, Applications, and Emerging Trends

Abstract

Multiple-criteria decision-making (MCDM) approaches have become vital for tackling complicated, multi-objective decision-making issues in dynamic and unpredictable situations. This paper covers the history of MCDM methodologies, ranging from conventional methods like the analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) to sophisticated innovations embracing fuzzy logic, hybrid models, and artificial intelligence (AI). It analyses the numerous uses of MCDM in business, engineering, healthcare, and environmental management, highlighting their adaptation to both qualitative and quantitative criteria. The approaches adopted include an examination of conventional and current frameworks, identifying their strengths and shortcomings. Emerging technologies such as integrating Blockchain, the Internet of Things, and big data analytics are studied, revealing their potential for real-time and dynamic decision-making. Key findings underscore the limitations of uncertainty modeling, computational complexity, and scalability while also revealing potential for multidisciplinary research and sustainability-focused applications. This study finds by offering actionable recommendations for researchers and practitioners, advocating for the establishment of AI-integrated, real-time decision-making frameworks and standardized evaluation standards to address contemporary challenges and enhance the practical relevance of MCDM methods.

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