A two-phase evolutionary algorithm for the university course timetabling problem

Course timetabling represents a difficult optimization problem and finding a high quality timetable is a challenging task. With a large number of events involved and various hard constraints to be fulfilled, finding an optimal timetable is complicated and time consuming. Conventional course scheduling is done with little attention to student´s interest and their needs in career development. This study develops a practical automatic course scheduling system based on student´s need, wherein the course scheduling process is divided into two stages. In the first stage, student´s needs in course selection are considered and a technique is used to establish dependency matrix of courses selected by students in the previous terms. In the second stage genetic algorithm with a new cycle crossover approach is used to arrange the courses into a timetable according to a cost function designed in this study. This study is based on student´s willingness in course selection, takes into account teacher preferences and then applies genetic algorithm, so as to produce as optimal course timetable. The automatic course scheduling system proposed in this study produce course timetables that truly fulfill user´s needs and increase students´ and teachers´ satisfaction. It is concluded that this cycle crossover operator in comparison with other types of crossover operators represents a particularly effective method for producing high quality solutions to the university course timetabling problem.