Advanced decision analytics with fuzzy logic integrating AI and computational thinking for personnel selection

Abstract

Advanced decision analytics, fuzzy logic, artificial intelligence, and computational thinking may be used to improve personnel selection in today's dynamic industry. This research introduces a decision-support framework that uses AI approaches based on fuzzy logic to handle complicated decision-making and improve personnel selection. Hiring the right person for the job is very significant for any business. However, numerous companies have Human Resources (HR) teams that deal with this issue. This study created a staff selection method using the Fuzzy Simple Additive Weighted (FSAW) Method, which considers the applicants' personalities and the fact that people are very subjective. The study aimed to find a method to hire people using fuzzy logic. A three-level plan was made to keep track of the information. Applicants would be ranked by how well they would fit the job. Personality was the most important thing to consider. Someone best does the job with the right skills and natural traits or abilities. Finally, the study showed that the best people can be hired, leading to work success. The results show that this hybrid strategy increases the consistency of decision-making while improving the accuracy, sensitivity and fairness of personnel selection across several case scenarios.

Keywords

• topsis method
• multicriteria problem
• z-number fuzzy aroman technique
• preliminaries
• aroman technique