Combination of graph heuristic with hill climbing search for solving capacitated examination timetabling problem
Arranging examination timetable is problematic. It differs from other timetabling problems in terms of conditions. A complete timetable must reach several requirements involving course, group of student sitting the exam in that course, etc. It is similar to the course's timetable but not the same. Many differences between them include the way to create and the requirements. This paper proposes an adaptive genetic algorithm model applied for improving effectiveness of automatic arranging examination timetable. Hard constraints and soft constraints for this specific problem were discussed. In addition, the genetic elements were designed and the penalty cost 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 an examination timetable. With 0.75 crossover rate, there is no hard constraints appeared in the timetable.