The timetabling of lecturers, seminars, practical sessions and examinations is a core business process for academic institutions. A feasible timetable must satisfy hard constraints. An optimum timetable will additionally satisfy soft constraints, which are not absolutely essential. An Ant Colony based Timetabling (ANCOT) tool has been developed for solving timetabling problems. New variants of Ant Colony Optimisation (ACO) called the Best-Worst Ant System (BWAS) and the Best-Worst Ant Colony System (BWACS) were embedded in the ANCOT program. Local Search (LS) strategies were developed and embedded into BWAS and BWACS to enhance their efficiency and to help find the best timetable with the lowest number of soft constraint violations. Statistical tools for experimental design and analysis were adopted to investigate the factors affecting the BWAS performance. Eight benchmark problems were used for evaluating the performance. For large problems, the BWACS produced the best timetable and was better than the other ACO variants. The best proposed local search strategy enhanced the performance of both the BWAS and the BWACS by up to 74.5%, but this was at the expense of longer execution time.
