A Survey Paper on Timetable Generator Using AI Methods

This research paper discusses an innovative Automated Timetable Generator leveraging the synergistic capabilities of AI and advanced optimization algorithms. Automated Timetable Generator leveraging Decision Tree, K-Means Clustering, and Random Forest algorithms for efficient scheduling. The Decision Tree algorithm is employed to classify and allocate time slots based on predefined constraints, ensuring that scheduling conflicts are minimized. K-Means Clustering is utilized to group subjects, faculty, and students based on similarities, optimizing resource allocation. The Random Forest model further enhances the accuracy and efficiency of scheduling by analyzing multiple possible allocations and selecting the best-fit timetable while ensuring fairness and balancing workload distribution. The Automated Timetable Generator aims to efficiently generate conflict-free timetables for second-year, third-year, and final-year engineering students, considering their divisions, subject allocations, available faculty, classrooms, and practical labs. This system automates the scheduling process, ensuring that no two subjects, teachers, or classrooms overlap while optimizing resource utilization. By reviewing different research paper, we identify the techniques, the automated timetable generator can efficiently handle complex scheduling requirements, reduce manual intervention, and produce balanced timetables that meet institutional constraints and preferences.