A Hybrid Mathematical Framework for Dynamic Incident Prioritization Using Fuzzy Q-Learning and Text Analytics

This paper presents a hybrid framework for dynamic incident prioritization in enterprise environments, combining fuzzy logic, natural language processing, and reinforcement learning. The proposed system models incident descriptions through semantic embeddings derived from advanced text analytics, which serve as state representations within a fuzzy Q-learning model. Severity and urgency are encoded as fuzzy variables, enabling the prioritization process to manage linguistic vagueness and operational uncertainty. A mathematical formulation of the fuzzy Q-learning algorithm is developed, including fuzzy state definition, reward function design, and convergence analysis. The system continuously updates its prioritization policy based on real-time feedback, adapting to evolving patterns in incident reports and resolution outcomes. Experimental evaluation on a dataset of 10,000 annotated incident descriptions demonstrates improved prioritization accuracy, particularly for ambiguous or borderline cases, and reveals a 19% performance gain over static fuzzy and deep learning-based baselines. The results validate the effectiveness of integrating fuzzy inference and reinforcement learning in incident management tasks requiring adaptability, transparency, and mathematical robustness.