University Timetabling Using Constraint Logic Programming
PADL '99 Proceedings of the First International Workshop on Practical Aspects of Declarative Languages
Some Complexity Aspects of Secondary School Timetabling Problems
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
A Language for Specifying Complete Timetabling Problems
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
Resource-Constrained Project Scheduling and Timetabling
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
Speedup learning for repair-based search by identifying redundant steps
The Journal of Machine Learning Research
A case study for timetabling in a dutch secondary school
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
Tabu search techniques for large high-school timetabling problems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Towards a deterministic model for course timetabling
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume II
Decomposition and parallelization of multi-resource timetabling problems
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
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The timetabling problem consists in fixing a sequence of meetings between teachers and students in a prefixed period of time (typically a week), satisfying a set of constraints of various types. A large number of variants of the timetabling problem have been proposed in the literature, which differ from each other based on the type of institution involved (university or high school) and the type of constraints. This problem, that has been traditionally considered in the operational research field, has recently been tackled with techniques belonging also to artificial intelligence (e.g. genetic algorithms, tabu search, simulated annealing, and constraint satisfaction). In this paper, we survey the various formulations of the problem, and the techniques and algorithms used for its solution.