Using a Randomised Iterative Improvement Algorithm with Composite Neighbourhood Structures for the University Course Timetabling Problem

The course timetabling problem deals with the assignment of a set of courses to specific timeslots and rooms within a working week subject to a variety of hard and soft constraints. Solutions which satisfy the hard constraints are called feasible. The goal is to satisfy as many of the soft constraints as possible whilst constructing a feasible schedule. In this paper, we present a composite neighbourhood structure with a randomised iterative improvement algorithm. This algorithm always accepts an improved solution and a worse solution is accepted with a certain probability. The algorithm is tested over eleven benchmark datasets (representing one large, five medium and five small problems). The results demonstrate that our approach is able to produce solutions that have lower penalty on all the small problems and two of the medium problems when compared against other techniques from the literature. However, in the case of the medium problems, this is at the expense of significantly increased computational time.