An Optimization Model for University Class Scheduling Using Genetic Algorithms: Room Assignment Efficiency at Father Saturnino Urios University

Genetic Algorithms (GAs) provide a robust metaheuristic framework for addressing complex academic scheduling problems by emulating natural evolutionary processes. This study applies a tailored GA to optimize room assignments for course sections at Father Saturnino Urios University, with dataset sources from the institution’s OPISv3 system. Key GA operations-selection, crossover, mutation, and adaptive termination-were configured to respect standard scheduling constraints and contextual room-subject requirements. In the first experiment, treating all rooms uniformly, the GA achieved an average conflict RMSE of 0.67, a conflict-free rate of 99.66%, and room assignment accuracy of $\mathbf{9 5. 8 3 \%}$. Introducing room subject-tagging in the second experiment marginally increased the RMSE to $\mathbf{1. 0}$ but raised assignment accuracy to 98.63%. These results confirm that embedding contextual constraints substantially improves timetabling precision, demonstrating the efficacy of GAs in resolving room conflict challenges in academic environments.