Complex environments tend to compel decision-making to consider a number of conflicting criteria at the same time. Multi-Criteria Decision-Making (MCDM) tools have been extensively applied to assist in facilitating structure decision-making in business management, engineering, healthcare, and the public policy arena. As the field of artificial intelligence (AI) is evolving rapidly, the conventional MCDM models are gradually being combined with machine learning, fuzzy logic, and evolutionary algorithms to enhance the accuracy and flexibility of decisions. MCDM models that utilize AI can help companies process large volumes of data, model uncertainty, and make complicated decisions through automation. In this paper, AI-based MCDM models and their uses in business and management decision-making are reviewed. The research points to the significant methods of artificial neural networks, fuzzy systems, evolutionary algorithms, and hybrid AI models. These results indicate that AI-based MCDM models can help to improve the quality and efficiency of the decisions considerably, but issues of model transparency, computational complexity, and data quality are also not negligible. The ways forward in future research involve creating explainable AI systems and hybrid intelligent decision support systems.
Swati Kumari (Mon,) studied this question.