A novel MCDM method for assessing student employability psychology using complex Pythagorean hesitant fuzzy Sugeno Weber aggregation.

Managing students' psychology in employability (MSPE) is essential because it allows students to use their skills and achieve successful professional careers. Researchers in this area face a substantial challenge due to many factors that support this problem. This article offers methods for perfectly arranging student information to achieve results in this disadvantage. Many other frameworks are available such as complex fuzzy hesitant set (CFHS) and complex intuitionistic fuzzy hesitant set (CIFHS), but these frameworks cannot handle this problem because the range of satisfactory degree (SFD) and dissatisfactory degree cannot fulfill the condition. To address this current problem of student management psychology in employability, this research provides a perfect system to manage this issue using multi-criteria decision-making (MCDM) based on a complex Pythagorean hesitant fuzzy set (CPyHFS). The MCDM approach is one of the best tools for selecting the best alternative. To improve the collection of CPyHFS, the Sugeno Weber t-norm, and t-conorm are supportive methods. The key objective of this research is to establish some novel aggregation operators as complex Pythagorean hesitant fuzzy Sugeno Weber weighted averaging (CPyHFSWWA) and complex Pythagorean hesitant fuzzy Sugeno Weber weighted geometric (CPyHFSWWG) operators and also its fundamental axioms. Then, we established the MCDM algorithm approach based on CPyHFS and briefly discussed a case study of MSPE. A numerical example is established to help select the best alternatives for MSPE. In the case study, take ten alternatives: personality development programs, soft skills training, internship opportunities, career guidance workshops, mentorship programs, online psychometric tests, peer support groups, stress management activities, counseling sessions, and mental health awareness campaigns based on four criteria, such as emotional intelligence, stress management, career guidance, and cognitive skills. When we use our proposed AOs to select the best alternatives. Career guidance workshops are the best alternative. We compare existing operators and the established approach to display our established approach's reliability, flexibility, and applicability. Lastly, we explain the conclusion of our research.