Optimized Automatic Timetable Generation Using Genetic Algorithm for Educational Institutions

Abstract: Timetable creation in schools and colleges is a complex, time‑consuming and error‑prone manual task.Administrators must assign teachers, subjects, rooms and time slots while satisfying numerous constraints such as faculty availability, room capacity, and avoiding clashes between classes, which makes the problem NP‑hard (Chen et al., 2021; Bashab et al., 2022). Manual scheduling often leads to conflicts, under‑utilization of resources and difficulty in adapting to changes during the semester (N, 2025; Bashab et al., 2022; Rudová et al., 2011). To address these challenges, this work proposes an automatic timetable generator based on a Genetic Algorithm (GA). The system models hard constraints (no overlaps, capacity, mandatory breaks) and soft constraints (teacher preferences, minimum gaps, balanced workload) in a fitness function and iteratively evolves candidate timetables using selection, crossover, and mutation (Dhomne, 2025; Dave et al., 2025; Katkar, 2024; Singhal et al., 2024). A web‑based interface allows users to input teachers, subjects, rooms and constraints, visualize generated timetables and regenerate schedules when requirements change. Experimental evaluation on sample institutional data indicates a significant reduction in generation time, fewer conflicts and improved room and faculty utilization compared to manual methods (Dhomne, 2025; Dave et al., 2025; Katkar, 2024; Singhal et al., 2024). The proposed system demonstrates that GA‑based automatic timetabling can provide a practical, scalable and user‑friendly solution for educational scheduling.

Keywords: Timetable Generation, Genetic Algorithm, Scheduling, Optimization, Academic Automation, Constraint Handling