A Randomised Iterative Improvement Algorithm with Composite Neighbourhood Structures for University Course Timetabling

The general course timetabling problem consists of assigning courses to a specific timeslot and room. The goal is to satisfy as many soft constraints as possible while constructing a feasible schedule (i.e. one that satisfies all the hard constraints). In this paper, we present a composite neighbourhood structure with a randomised iterative improvement algorithm. Many relevant publications can be found in the literature. For example, see the volumes of papers from the international conferences on the Practice and Theory of Automated Timetabling (in Burke and Ross 1996, Burke and Carter 1998, Burke and Erben 2001 and Burke and Causmaecker 2003). Comprehensive surveys on timetabling can be found in de Werra (1985), Burke et al. (1997), Schaerf (1999), Burke and Petrovic (2002) and Petrovic and Burke (2004). The approach that we present in this abstract is tested over eleven benchmark datasets. The results demonstrate that our approach is able to produce solutions that are better than others that appear in the literature.