AI-Powered Academic Subject Allocation and Intelligent Timetable Generation System Using Priority Algorithms and Constraint Satisfaction techniques

Efficient management of academic timetables and elective subject allocation is a complex and constraint-intensive problem in higher education institutions. Traditional manual and semi-automated approaches are often time-consuming, error-prone, and incapable of ensuring fairness and optimal resource utilization. This paper proposes an automated timetable generation and elective subject allocation system that integrates constraint satisfaction problem (CSP) modeling with priority-based and fair allocation algorithms. The proposed system employs CSP techniques to generate conflict-free weekly timetables by simultaneously considering faculty availability, classroom constraints, subject distribution, and time-slot conflicts. In parallel, elective subject allocation is handled using First Come First Served (FCFS), Round Robin, and Priority-Based Allocation algorithms, where student preferences are weighted using academic performance indicators such as CGPA or qualifying examination percentage. The system is implemented using a modular web-based architecture comprising Spring Boot for application logic, MySQL for persistent data management, and a Python-based Flask API leveraging Google OR-Tools for constraint solving. Experimental evaluation demonstrates that the proposed approach significantly reduces scheduling conflicts, improves fairness in elective allocation, and minimizes administrative workload when compared to conventional methods. The results validate the effectiveness, scalability, and adaptability of the system in real academic environments. The proposed framework provides a robust foundation for intelligent academic scheduling and can be extended to support dynamic constraints, real-time updates, and advanced optimization techniques in future deployments.