Abstract
This paper examines the application of neural networks as a construction heuristic for the examination timetabling problem. Building on the heuristic ordering technique, where events are ordered by decreasing scheduling difficulty, the neural network allows a novel dynamic, multi-criteria approach to be developed. The difficulty of each event to be scheduled is assessed on several characteristics, removing the dependence of an ordering based on a single heuristic. Furthermore, this technique allows the ordering to be reviewed and modified as each event is scheduled; a necessary step since the timetable and constraints are altered as events are placed. Our approach uses a Kohonen self organising neural network and is shown to have wide applicability. Results are presented for a range of examination timetabling problems using standard benchmark datasets.
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Corr, P.H., McCollum, B., McGreevy, M.A.J., McMullan, P. (2006). A New Neural Network Based Construction Heuristic for the Examination Timetabling Problem. In: Runarsson, T.P., Beyer, HG., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds) Parallel Problem Solving from Nature - PPSN IX. PPSN 2006. Lecture Notes in Computer Science, vol 4193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11844297_40
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DOI: https://doi.org/10.1007/11844297_40
Publisher Name: Springer, Berlin, Heidelberg
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