AI-Based Automated Timetable Generation System Using Optimization Algorithms

Abstract

This paper presents an AI-based automated timetable generation system designed to efficiently create conflict-free schedules for educational institutions. Traditional manual timetable generation is time-consuming, error-prone, and lacks optimization. The proposed system utilizes advanced scheduling algorithms such as Constraint Satisfaction Problem (CSP), Genetic Algorithm (GA), and OR-Tools to generate optimized timetables. The system considers multiple constraints including faculty availability, classroom allocation, subject distribution, and time slot management. A web-based interface allows administrators to manage data and generate timetables, while faculty and students can view schedules. Conflict detection and resolution mechanisms ensure that no overlapping assignments occur. The system also supports exporting timetables into PDF and Excel formats. Experimental results show improved efficiency, reduced manual effort, and better resource utilization. This approach provides a scalable and intelligent solution for academic scheduling problems.