Nonlinear Optimization Framework for Educational Timetable Scheduling using Differential Evolution

Abstract

This research presents a nonlinear model for optimizing educational timetables using the Differential Evolution algorithm. The model manages complex scheduling tasks by considering various constraints, including teacher availability, course requirements, classroom features, and institutional rules. It processes data through feature engineering to quantify factors like time slots, teacher expertise, and course priorities. The algorithm dynamically explores multiple timetable configurations, evaluating their suitability based on hard constraints like teacher availability and classroom occupancy, and soft constraints such as minimizing idle periods and meeting preferences. Experimental analysis shows that the model improves scheduling by resolving conflicts, reducing idle hours, and utilizing classroom space more efficiently. The findings highlight a structured approach to balancing institutional needs and logistical challenges in timetable generation.