A novel 4D hybrid decision-making approach and its applications in supplier selection problem

Supplier selection is not just a challenge; it’s a critical strategic decision that significantly impacts an organization’s performance. The complexity arises from the involvement of multiple experts in decision-making, each with their own perspectives and priorities. This diversity often leads to conflicts and uncertainties that must be navigated effectively. One of the key gaps in existing approaches to supplier selection lies in handling these conflicting opinions among experts. To address the gap a novel 4-dimensional hybrid decision-making approach is proposed. By incorporating dynamic feedback mechanisms, dominance considerations, distance metrics, and degree measures, this framework provides a comprehensive solution to the challenges faced in multi-attribute group decision-making. The use of fuzzy set theory, particularly Triangular Fuzzy Numbers (TFNs), is pivotal in capturing the inherent vagueness and uncertainty present in expert’s initial opinions. This allows for a more nuanced and realistic representation of expert’s perspectives, enhancing the accuracy of the decision-making process. A key feature of the proposed approach is the implementation of an attitudinal consensus threshold (ACT) as part of the dynamic feedback mechanism. This threshold mechanism ensures that evolving opinions and consensus among experts are effectively incorporated into the decision-making process, making the model adaptable to changing circumstances and preferences. Moreover, the framework integrates various measures, including preference relations, distance metrics, and degree measures between alternatives. This holistic approach not only establishes a clear ranking order for alternatives but also provides a deeper understanding of the decision landscape, allowing experts to make more informed and effective choices. To validate the practicality and effectiveness of the proposed methodology, a real-world case study focusing on supplier selection is presented. Through comparative and sensitivity analyses, the research demonstrates how the proposed approach outperforms traditional methods, highlighting its potential to revolutionize decision-making processes in dynamic business environments.