School Timetable Optimization Model Using Genetic Algorithms Considering Hard and Soft Constraints

This work presents a Hybrid Genetic Al-gorithm (HGA) for automatic school timetable gen-eration at a private institution in Quito, Ecuador. The model incorporates hard constraints (e.g., teacher conflicts, pedagogical continuity) and soft constraints (e.g., part-time staff, maternity leave), representing the complete timetable as a chromosome with 2,832 genes. An eight-phase methodological pipeline was developed, covering data preparation, chromosome generation, fit-ness evaluation, and solution verification. Two experi-ments were conducted: one with random initialization and another using a historical timetable as a heuristic seed. The heuristic-enhanced approach achieved a valid schedule (fitness =1) in under 12 hours, reducing plan-ning time from 10 - 15 days to less than 24 hours. The system improves schedule coherence, supports yearly reuse, and is adaptable to other institutions through structural adjustments.