The University Course Timetabling Problem (UCTP) is a scheduling problem of assigning teaching event in certain time and room by considering the constraints of university stakeholders such as students, lecturers, and departments. The constraints could be hard (encouraged to be satisfied) or soft (better to be fulfilled). This problem becomes complicated for universities which have an immense number of students and lecturers. Moreover, several universities are implementing student sectioning which is a problem of assigning students to classes of a subject while respecting individual student requests along with additional constraints. Such implementation enables students to choose a set of preference classes first then the system will create a timetable depend on their preferences. Subsequently, student sectioning significantly increases the problem complexity. As a result, the number of search spaces grows hugely multiplied by the expansion of students, other variables, and involvement of their constraints. However, current and generic solvers failed to meet scalability requirement for student sectioning UCTP. In this paper, we introduce the Multi-Depth Genetic Algorithm (MDGA) to solve student sectioning UCTP. MDGA uses the multiple stages of GA computation including multi-level mutation and multi-depth constraint consideration. Our research shows that MDGA could produce a feasible timetable for student sectioning problem and get better results than previous works and current UCTP solver. Furthermore, our experiment also shows that MDGA could compete with other UCTP solvers albeit not the best one for the ITC-2007 benchmark dataset.
Learning assignment order in an ant colony optimiser for the university course timetabling problem
