AI-Driven Dynamic Scheduling and Real-Time Notification System for Staff Optimization and Conflict Resolution

In institutions, developing a timetable is a challenging and problematic process, particularly when creating it manually. When managing multiple departments, some common challenges arise like overlapping schedules, unequal staff allocation for classes and insufficient classroom usage. This proposed work presents an AI-driven solution to automate and optimize timetable scheduling in colleges by using intelligent algorithms. The research involves proposed methodologies that include Constraint Satisfaction Problem (CSP) solutions and Genetic Algorithms, allowing the system to allocate courses, faculty members, classrooms and time slots without any conflicts. This model provides an equal and optimal distribution by considering several limitations like faculty availability, workload balance and institution regulations. The proposed research uses the Genetic Algorithm that represents timetables as “chromosomes” evaluated using a fitness function based on moderate or severe limitations producing solutions through crossover and mutation. The system increases the accuracy of scheduling by reducing human effort. It also handles faculty or time alterations in real-time. This smart automation method improves scalability across institutions and creates a path for a smart educational management system. The proposed system provides a significant shift from manual techniques to data-driven scheduling, increasing educational planning and utilizing resources effectively.