A Constraint Driven Approach to Automated Academic Timetable Scheduling

The proposed AI-Based Timetable Generator can be considered a powerful tool in solving the issue of timetable generation in educational institutions. By using the principles of Artificial Intelligence and the Genetic Algorithm, this system effectively generates optimized and conflict-free timetables. The experimental results show that the proposed model successfully solves the problem of scheduling, allowing the generation of efficient timetables with a minimum number of scheduling conflicts. This method demonstrates its superiority over conventional solutions as it is more accurate, flexible, scalable, and efficient than traditional manual or rule-based solutions.   As for future improvements, it is possible to consider adding various machine learning techniques to predict and adaptively generate timetables. In addition, the development of a mobile app can help make the process more accessible to users. Moreover, the system should be integrated with various academic platforms for attendance tracking and student data handling. The use of dynamic scheduling functions can assist in handling unpredictable changes and ensuring timely modifications. Thus, the use of cloud technology can help enhance the efficiency of the developed algorithm.

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