25.11.21IE

Scheduling in the education services industry presents a complex multi-objective optimization challenge, where minimizing faculty workload, maximizing student satisfaction, and reducing operational cost often conflict. This study develops a novel multi-objective optimization model to bridge this gap. Empirical analysis using 1,284 scheduling records, workload data from 171 teachers, 9,462 student satisfaction surveys, and institutional cost accounts reveals significant correlations: faculty workload negatively correlates with student satisfaction (\u03b2 = -0.426, p < 0.001) and positively with operational cost (\u03b2 = 0.513, p < 0.001). A genetic algorithm-based solution demonstrates simultaneous improvements: 16.2% reduction in faculty workload, 4.5% increase in student satisfaction, and 8.3% reduction in operational cost compared to baseline. The study's innovation lies in integrating educational management with operations research, providing an empirically validated decision support tool for enhancing educational service efficiency and resource allocation.