A Hybrid Approach to Automatic Timetabling Using Self-Organizing Maps, Secure Convex Dominating Sets, and Metaheuristics

Creating conflict-free academic timetables that respect teacher availability, subject eligibility, and limited re-sources remains a persistent challenge in educational institutions. This study introduces a novel hybrid algorithm that combines Self-Organizing Maps (SOM), Secure Convex Dominating Sets (SCDS), and Genetic Algorithms (GA) to address this problem effectively. SOM is employed to cluster subjects based on teaching duration and eligibility, providing structured guidance in initial scheduling. SCDS identifies the most conflict-prone subjects—typically those with limited eligible teachers—and ensures they are prioritized, thereby reducing downstream bottlenecks. GA then iteratively refines the schedule by evaluating room assignments, teacher loads, and constraint satisfaction. Extensive simulation experiments were conducted under varying conditions, including worst-case scenarios with dense scheduling conflicts. The system achieved high success rates, particularly in moderate to complex settings, and demonstrated robustness even in constrained environments. Notably, SOM improved spatial and temporal coherence, while SCDS enhanced conflict resolution and GA enabled adaptive optimization. Runtime and convergence results remained within practical limits, with a time complexity of O(n2+gpn). The proposed hybrid framework balances structural prioritization and evolutionary refinement, offering a scalable and intelligent solution to the timetabling problem. It stands out by gracefully handling worst-case scenarios where traditional heuristics often fail.

@article{Ranolo2025,
  title     = {A Hybrid Approach to Automatic Timetabling Using Self-Organizing Maps, Secure Convex Dominating Sets, and Metaheuristics},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Elmo Ranolo and Ken Gorro and Pierre Anthony Gwen Abella and Lawrence Roble and Rue Nicole Santillan and Anthony Ilano and Benjie Ociones and Roel Vasquez and Deofel Balijon and Daniel Ariaso Sr. and Rose Ann Campita and Robert Jay Angco},
  doi       = {10.14569/IJACSA.2025.0160805},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160805}
}