On Improvement of Effectiveness in Automatic University Timetabling Arrangement with Applied Genetic Algorithm

Arranging university course´s timetable is problematic. It differs from other timetabling problems in terms of conditions. A complete university timetable must reach several requirements involving students, subjects, lecturers, classes, laboratory´s equipments, etc. This paper proposes a genetic algorithm model applied for improving effectiveness of automatic arranging university timetable. Hard constraints and soft constraints for this specific problem were discussed. In addition, the genetic elements were designed and the fitness function was proposed. Three genetic operators: crossover, mutation, and selection were employed. A simulation was conducted to obtain some results. The results show that the proposed GA model works well in arranging a university timetable. With 0.70 crossover rate, there is no hard constraints appeared in the timetable.