An Integrated Weighted Distance-Based Approximation Method for Interval-Valued Spherical Fuzzy MAGDM

Interval-valued spherical fuzzy (IVSF) sets are an effective tool for dealing with uncertainty due to their broader space. The score functions are commonly used to distinguish the IVSF sets. But the existing score functions of IVSF sets cannot distinguish the IVSF sets properly. So, the purpose of this chapter is to introduce a noble score function within the IVSF context and apply it to multi-attribute group decision-making (MAGDM). Also, we establish that the proposed score function easily overcomes the limitations of the current score functions. Then, we develop Euclidean distance measure in the context of an IVSF environment. Next, we extend the weighted distance-based approximation (WDBA) under IVSF information to solve MAGDM problems. To estimate the weights of decision experts, we utilize the analytical hierarchy process. To determine the attribute weights, we develop the entropy method utilizing the proposed score function. Then, we solve a supplier selection problem for a renowned textile manufacturing company to illustrate the practicality and efficiency of the proposed model. In the numerical example, we consider five suppliers and the six important attributes by taking the opinions of three decision experts. According to the findings of this study, the first supplier is the best for the textile manufacturing industry. Finally, we compared the results to several existing methods to demonstrate the feasibility of our model.