Creating academic timetables for educational institutions is a complex and time-consuming task that must account for constraints such as subject requirements, teacher availability, classroom resources, and schedule conflicts. This research presents the design and implementation of an automated school timetable generation system using Python and the Flask web framework. The system enables administrators to input parameters, including teacher-subject assignments, class sections, and weekly time limits. It employs a hybrid scheduling algorithm that combines constraint-based logic with randomized and greedy allocation techniques to generate conflict-free schedules. Comparative evaluation was conducted against two existing scheduling approaches—a standard genetic algorithm and a simulated annealing model—using datasets from three different schools. The proposed system achieved an average scheduling accuracy of 96.3% and a generation time 28% faster than the best-performing baseline. It also demonstrated higher adaptability in handling complex constraints such as laboratory sessions and skill-based periods. These results confirm the model's effectiveness for small-to medium-sized institutions, offering a flexible, scalable, and computationally efficient alternative to manual and conventional automated scheduling methods. The automation substantially reduces administrative workload while improving consistency and efficiency in academic planning.
Joe Bryan (Wed,) studied this question.