In this chapter, we introduce a new extension of Complex Proportional Assessment (COPRAS) method based on q-Rung Orthopair Fuzzy Sets which successfully deal with the uncertainty of real-life subjective decisions. One of the most important components of a long-term business relationship in supply chain management is to evaluate and choose an appropriate supplier. The proposed methodology is tested on the generated synthetic data. As a result, a data-driven MCGDM methodology is proposed where the weights of the DMs are calculated by using an ML algorithm and heterogeneous information is aggregated by the CBD approach. The information provided in intuitionistic fuzzy numbers, hesitant fuzzy linguistic terms, and hesitant fuzzy numbers is converted to belief degrees to find the final rankings of the alternatives. In this study, to deal with such heterogeneous information cases, the cumulative belief degree (CBD) approach based on belief structure and fuzzy linguistic term is proposed. In Multi-Criteria Group Decision-Making (MCGDM) problems, DMs may provide their evaluations in different formats. Subsequently, the weights are calculated for the given problem on hand, according to the characteristics of the DMs involved. For this, initially, an ML algorithm is designed to find the relations between the weights of the DMs and their characteristics, such as age and experience, using the weight schemes applied in previous GDM processes. In this study, a data-driven methodology is proposed to find the weights of DMs by using a machine learning (ML) method. In the literature, although there are many studies on criteria weights, the number of studies on DM weights is limited. When DMs have varied contributions because of their expertise, experience, consistency with others, etc., they are assigned weights to incorporate their value in the final result. The success of the GDM processes is directly related to the evaluations of the decision makers (DMs). Due to the complex decision-making problems involving multiple stakeholders, the interest in group decision-making GDM approaches increases.

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