A New Adaptive Heuristic Framework for Examination Timetabling Problems

Heuristic construction methods, very similar to those used for graph colouring problems, have long been applied successfully to the examination timetabling problem. Despite the success of these methods on real life problems, even with limited computing resources, the approach has the fundamental flaw that it is only as effective as the heuristic that is used. One of the motivations of this paper is to attempt to develop approaches that can operate at a higher level of generality and that can adapt heuristics to suit the particular problem instance in hand. Indeed, the main aim of this paper is not to “beat” published results on benchmark problems by special purpose heuristics or meta-heuristics but to develop a more general system that does not depend on one particular heuristic approach but can still obtain results that are comparable with special purpose heuristics. We present an adaptive framework that adapts to suit a particular problem instance “on the fly”. This framework provides an alternative to existing forms of ‘backtracking’, which are often required to cope with the possible unsuitability of a heuristic. We present a range of experiments on benchmark problems to test and evaluate the approach. In comparison to other published approaches to solving this problem, the adaptive framework presented in this paper is more general, significantly quicker and easier to implement and produces results that are at least comparable (if not better) than the current state of the art. We also demonstrate the level of generality of this approach by starting it with the inverse of a known good heuristic and a null ordering and by showing that the adaptive method can transform a bad heuristic ordering into a good one.