Integrating Fuzzy Logic with the Analytic Hierarchy Process for Workforce Allocation Decisions in the Jordanian Industrial Sector is a research study authored by Yaqin Ibrahem Saed Alassaf and Malek Khalef Albezuirat. The study develops and validates a Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) framework to support workforce allocation decisions in the Jordanian industrial sector under conditions of uncertainty and subjective managerial judgment. The research addresses the limitations of the classical Analytic Hierarchy Process (AHP), which requires precise numerical comparisons that may not accurately represent real-world expert evaluations. To overcome this challenge, fuzzy logic based on triangular fuzzy numbers was integrated into the AHP framework using Buckley’s geometric mean method. Data were collected through expert interviews and pairwise comparison surveys involving industrial managers and engineers from pharmaceutical, construction-product, and cement companies in Jordan. The analysis identified six key workforce allocation criteria: Experience, Technical Skills, Human and Behavioral Factors, Operational Factors, Organizational Factors, and Logistical Factors. The results demonstrated that Experience was the most influential criterion, followed by Operational and Technical factors, with consistent ranking across both classical AHP and fuzzy-based analysis. Sensitivity analysis using alpha-cut levels confirmed the robustness of the model, showing that the proposed Fuzzy-AHP approach provides an effective, transparent, and uncertainty-aware decision-support tool for improving workforce allocation and enhancing industrial productivity in the context of Industry 4.0 transformation. Keywords: Analytic Hierarchy Process (AHP), Fuzzy Logic, Fuzzy-AHP, Workforce Allocation, Multi-Criteria Decision-Making (MCDM), Triangular Fuzzy Numbers, Industrial Management, Industry 4.0, Decision Support Systems, Jordanian Industrial Sector.