Article Preview
TopIntroduction
Multi-attribute group decision-making (MAGDM) is a decision-making process that addresses complex problems involving multiple criteria or attributes and incorporates the opinions of a group of decision-makers (Zhou et al., 2024b, 2024c; Zhu et al., 2024; Zorlu et al., 2024). It is widely used in various fields, such as education, health care, business, and engineering, where decisions must consider diverse perspectives and conflicting objectives (Liang et al., 2025; Wang et al., 2023, 2024a; Xu et al., 2024b; Yang et al., 2024b). In MAGDM, each decision-maker evaluates alternatives based on a set of predefined attributes, which may include quantitative or qualitative factors (Liu et al., 2024; Wang & Du, 2024; Wang & Feng, 2024; Wang et al., 2024b; Wei et al., 2024; Wu et al., 2024). These attributes are often assigned weights to reflect their relative importance in the decision-making process. The challenge lies in aggregating individual preferences and resolving inconsistencies among group members to arrive at a consensus or a ranked list of alternatives. Techniques such as entropy (Sun, 2024; Yang et al., 2024a; Yue et al., 2024), analytic hierarchy process (Wang et al., 2024c; Yang et al., 2024c; Zhang & Lee, 2024; Zhang & Tak, 2024; Zhou et al., 2024a), and fuzzy logic are commonly used to handle subjective judgments, imprecise data, and attribute weighting. MAGDM is particularly valuable in scenarios where decisions must balance various factors, such as cost, quality, and efficiency (Meng et al., 2024a, 2024b, 2024c; Mittermeier et al., 2024; Qiu et al., 2024; Rahim et al., 2024; Rawat et al., 2024). It enables collaborative decision-making by integrating diverse viewpoints, ensuring that the final decision is comprehensive and well-informed. Furthermore, advanced MAGDM methods, such as those using intuitionistic fuzzy sets (Burillo & Bustince, 1996; Varshney et al., 2022; Xie et al., 2022) or neutrosophic sets (Wang, 2024; Yang et al., 2024d, 2024e; Zaidan et al., 2024; Zheng et al., 2024), are increasingly applied to handle uncertainty and ambiguity in complex, real-world problems.