An Intelligent Decision Support System for Human Resource Planning Using Fuzzy Logic and AHP Techniques

Human Resource Planning (HRP) is an important part of running a business that makes sure there are enough people with the relevant abilities available when required. Because people are subjective and it's hard to judge qualitative elements, traditional HRP approaches typically don't make decisions that are very accurate. This study suggests a new and unified technique that combines Fuzzy Logic with the Analytic Hierarchy Process (AHP) to create an intelligent Decision Support System (DSS) that makes human resource planning more reliable and accurate. The method employs fuzzy sets to show how unclear and vague human judgment may be, and it also ranks numerous HR factors, including as skill levels, performance, experience, and organizational needs, in order of priority. The system helps you be ready for recruiting, assigning staff, and succession in a manner that is orderly, scalable, and adaptable. This used a real-world HR dataset to evaluate the created DSS, and it showed huge improvements in how effectively it planned and how consistently it made judgments. The proposed model is useful for HR in making better judgments in circumstances that are hard to understand and convoluted.