A Constraint Programming-based Approach for Personalized Course Timetabling Optimization

This study proposes a constraint programming (CP) model to generate personalized course timetables for university students, addressing the limitations of existing university course timetabling problems (UCTP). The proposed model incorporates both hard constraints and student preferences including inter-building transition times and course ratings, to optimize timetable combinations. In this paper, we demonstrate the effectiveness of the model in successfully generating multiple high-quality timetabling solutions using curriculum data from an actual university. A sensitivity analysis reveals that the CP model consistently outperforms traditional Mixed Integer Linear Programming (MIP) and metaheuristic models in terms of solution quality and computational efficiency. The proposed model achieves search times 1.5-4 times faster than existing methods while maintaining solution diversity. The model's declarative constraint handling and efficient search capabilities make it particularly suitable for online service implementation. This research contributes to the academic literature by introducing a novel CP-based approach to optimize students’ timetables, and demonstrates its practical applicability to improve students’ academic planning and satisfaction.