Hybridizing Meta-heuristics Approaches for Solving University Course Timetabling Problems

Abstract

In this paper we have presented a combination of two meta-heuristics, namely great deluge and tabu search, for solving the university course timetabling problem. This problem occurs during the assignment of a set of courses to specific timeslots and rooms within a working week and subject to a variety of hard and soft constraints. Essentially a set of hard constraints must be satisfied in order to obtain a feasible solution and satisfying as many as of the soft constraints as possible. The algorithm is tested over two databases: eleven enrolment-based benchmark datasets (representing one large, five medium and five small problems) and curriculum-based datasets used and developed from the International Timetabling Competition, ITC2007 (UD2 problems). A new strategy has been introduced to control the application of a set of neighbourhood structures using the tabu search and great deluge. The results demonstrate that our approach is able to produce solutions that have lower penalties on all the small and medium problems in eleven enrolment-based datasets and can produce solutions with comparable results on the curriculum-based datasets (with lower penalties on several data instances) when compared against other techniques from the literature.