Development of an Optimized Timetable Scheduling for Efficient Resource Utilization

Abstract

This study aims to optimize timetable scheduling for efficient resource utilization in educational institutions. The objectives were to minimize scheduling conflicts, maximize resource usage, and ensure equitable workload distribution. A hybrid Genetic Algorithm–Particle Swarm Optimization (GA-PSO) model was developed and implemented in Python using The Federal Polytechnic Ado-Ekiti dataset. The method combines GA’s global search capability with PSO’s fast convergence to enhance solution quality. Results show that the hybrid model outperformed standalone approaches, achieving a 71.6% reduction in conflict rate, an 8.8% increase in resource utilization, and a fairness index of 0.94 with reduced computation time. These findings demonstrate the model’s robustness, scalability, and efficiency in handling complex scheduling problems. The study concludes that hybrid metaheuristic approaches provide a reliable framework for intelligent scheduling and recommends their adoption with continuous tuning and real-time integration for improved institutional performance.