Metaheuristics for High School Timetabling
Computational Optimization and Applications
On a multiconstrained model for chronmatic scheduling
Proceedings of the third international conference on Graphs and optimization
Tabu Search
Hopfield neural networks for timetabling: formulations, methods, and comparative results
Computers and Industrial Engineering - Special issue: Focussed issue on applied meta-heuristics
A Constructive Evolutionary Approach to School Timetabling
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Automated Solution of a Highly Constrained School Timetabling Problem - Preliminary Results
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
A Hybrid Genetic Algorithm for School Timetabling
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Recent Developments in Practical Course Timetabling
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
A survey of very large-scale neighborhood search techniques
Discrete Applied Mathematics
A survey of automated timetabling
A survey of automated timetabling
A tiling algorithm for high school timetabling
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
Local search techniques for large high school timetabling problems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Cyclic transfers in school timetabling
OR Spectrum
A simulated annealing based approach to the high school timetabling problem
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
The consultation timetabling problem at Danish high schools
Journal of Heuristics
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This paper describes a case study for constructing the yearly schedule of a secondary school in the Netherlands. This construction is divided in three steps. In the first step we create cluster schemes containing the optional subjects. A cluster scheme consists of cluster lines, and a cluster line contains classes which will be taught simultaneously. Part of the problem is that the students are not yet assigned to the classes. Once the cluster schemes are fixed, it remains to schedule the lessons to time slots and rooms.We first schedule the lessons to day-parts, and once this is completed we schedule the lessons to time slots within the day-parts. Thanks to consistency checks in the day-part phase, going from day-parts to time slots is possible. Finally, in the third step, we improve the previously found schedule by a tabu search using ejection chains. Compared to hand-made schedules, the results are very promising.