Timetabling problems is one of very significant problems in many fields of applications. As mentioned in [3], this problem is NP-complete with numerous factors and constraints. In general, it is treated as a multi-objective optimization problem. Currently, the work of scheduling is very difficult in universities, especially in credits training. Almost it is impossible to control all cases of the problem by human. Therefore, we cannot manually give an effective solution for this problem. There are quite many methods to resolve this problem in literature, they are mostly searching methods based on genetic algorithms and their results are proved effective in practice. In this paper, we propose a method based on genetic algorithms for university course timetabling problems with some modifications and apply it to real-world datasets in Hanoi Open University.

Universities usually use academic credit systems for holding all training courses. They have to establish a suitable timetable for enrollment by students at beginning of every semester. This timetable must be met to all hard constraints and it is satisfied to soft constraints as high as possible. In some universities, students can enroll to the established timetable so that among of their courses is as much as possible. This leads to finish their studying program earlier than normally cases. In addition, this also leads to well-utilized resources such as facilities, teachers and so forth in universities. However, a timetable usually has so many courses and some its courses have same subjects but different time-slots. These may cause difficulties for manually enrolling by students. It may be fall into conflict of time when choosing two courses at same time-slots. It is difficult for enrollment with high satisfied. In this paper, we design a genetic algorithm based method for university timetable with maximal enrollments by using maximum matching on bipartite graphs.

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