The effect of neighborhood structures on tabu search algorithm in solving university course timetabling problem

The design of course timetables for academic institutions is a very difficult job due to the huge number of possible feasible timetables with respect to the problem size. This process contains lots of constraints that must be taken into account and a large search space to be explored, even if the size of the problem input is not significantly large. Different heuristic approaches have been proposed in the literature in order to solve this kind of problem. One of the efficient solution methods for this problem is tabu search. Different neighborhood structures based on different types of move have been defined in studies using tabu search. In this paper, different neighborhood structures on the operation of tabu search are examined. The performance of different neighborhood structures is tested over eleven benchmark datasets. The obtained results of every neighborhood structures are compared with each other. Results obtained showed the disparity between each neighborhood structures and another in terms of penalty cost.