Selection of Optimal Clinic using Proportional Fermatean Fuzzy TOPSIS Technique

Proportional Fuzzy Sets (PFSs) address real-world uncertainty using proportions instead of exact decimal values, offering a more flexible and intuitive approach to represent partial membership. This paper introduces Proportional Fermatean FS (PFFS), an extension of Fermatean FS (FFS). PFFSs use proportional relationships to determine membership and non-membership degrees, thereby improving accuracy and reliability under uncertain environments. This paper combines PFFSs with the TOPSIS method, a widely used MCDM technique. The PFFS-TOPSIS combination offers a more effective decision-making process, especially in cases where the criteria are vague or uncertain. To demonstrate its practical application, the paper uses the PFFS-TOPSIS method in a clinic selection problem, where criteria like cost, quality of service, and location may have imprecise values. Sensitivity and comparative analyses validate the PFFS-TOPSIS method, demonstrating its robustness, flexibility, and superior performance in handling uncertainty, accuracy, and efficiency compared to traditional decision-making techniques in complex problems.