Adaptive linear combination of heuristic orderings in constructing examination timetables

In this paper, we investigate an adaptive linear combinations of graph coloring heuristics with a heuristic modifier for solving the examination timetabling problem. We invoke a normalisation strategy for each parameter in order to generalise the specific problem data. Two graph coloring heuristics were used in this study (largest degree and saturation degree). A score for the difficulty of assigning each examination was obtained from an adaptive linear combination of these two heuristics and examinations in the list were ordered based on this value. The examinations with the higher difficulty score value were chosen for scheduling based on two strategies. We have tested for single and multiple heuristics with and without a heuristic modifier with different combinations of weight values for each parameter on the Toronto and ITC2007 benchmark data sets. We have observed that the combination of multiple heuristics with a heuristic modifier offers the most effective way to obtain good solution quality. Experimental results showed that our approach has obtained promising results. We conclude that this adaptive linear combination of heuristics is a highly effective method and simple to implement.