Automatic Timetabling Using Artificial Immune System
University timetabling problem is a very common and seemingly simple, but yet very difficult problem to solve in practice. While solution definitely exists (evidenced by the fact that we do hold classes), an automated optimal schedule is very difficult to derive at present. There were successful attempts to address this problem using heuristics search methods. However, until now, university timetabling is still largely done by hand, because a typical university setting requires numerous customized complicated constraints that are difficult to model or automate. In addition, there is a problem of certain constraints being inviolable, while others are merely desirable. This paper intends to address the university timetabling problem that is highly constrained using Artificial Immune System. Empirical study on course timetabling for the School of Computer Engineering (SCE), Nanyang Technological University (NTU), Singapore as well as the benchmark dataset provided by the Metaheuristic Network shows that our proposed approach gives better results than those obtained using the Genetic Algorithm (GA).
KeywordsGenetic Algorithm Benchmark Problem Soft Constraint Hard Constraint Timetabling Problem
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- 2.Burke, E.K., Elliman, D.G., Weare, R.F.: A university timetabling system based on graph colouring and constraint manipulation. Journal of Research on Computing in Education 27(1), 1–18 (1994)Google Scholar
- 4.Burke, E.K., Eckersley, A., McCollum, B., Petrovic, S., Qu, R.: Using simulated annealing to study behaviour of various exam timetabling data sets. In: Proceedings of the Fifth Metaheuristics International Conference (MIC 2003), Kyoto, Japan (August 2003)Google Scholar
- 5.Jaumard, B., Cordeau, J.-F., Morales, R.: Efficient Timetabling Solution with Tabu Search. Avaliable from Metaheuristics Network - Intenational Timetabling Competition (2003), http://www.idsia.ch/Files/ttcomp2002/jaumard.pdf
- 6.Chiarandini, M., Socha, K., Birattari, M., Rossi-Doria, O.: An effective hybrid approach for the university course timetabling problem. Journal of Scheduling (2003) (to appear)Google Scholar
- 8.Metaheuristics Network, http://www.metaheuristics.org/
- 9.Dasgupta, D., Ji, Z., González, F.: Artificial immune system (ais) research in the last five years. In: Proceedings of the International Conference on Evolutionary Computation Conference (CEC), Canbara, Australia (December 2003)Google Scholar
- 10.de Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems 6(3), 239–251 (2002)Google Scholar
- 11.de Castro, L.N., Timmis, J.I.: Artificial immune system as a novel soft computing paradigm. Soft Computing 7(8), 526–544 (2003)Google Scholar
- 12.University Course Timetabling Benchmark Solution Score Calculation, http://www.idsia.ch/Files/ttcomp2002/IC_Problem/node2.html