TSUGA & GA: A PREDICTIVE MODEL FOR CLASS & FACULTY SCHEDULING SYSTEM

Abstract

This study explores the Timetable Scheduling System using Genetic Algorithm to automate and optimize scheduling at Southwestern University PHINMA, addressing inefficiencies in the manual process. Using a constraint-based approach, the system analyses faculty availability, room capacity, subject requirements, and time slot preferences to generate optimal schedules while ensuring compliance with institutional policies. The model employs constraint modelling, solution space reduction, and heuristic-based optimization, resolving 98% of scheduling conflicts and reducing timetable generation time from days to minutes. User feedback indicates high satisfaction (usability: 4.5/5, accuracy: 4.7/5), with administrators reporting a 75% reduction in manual intervention. The study provides a scalable framework for academic institutions transitioning to automation, proving particularly effective in managing last-minute adjustments. Future research could explore hybrid methods combining constraint satisfaction with metaheuristics to further enhance scheduling accuracy and adaptability in larger-scale implementations.