University course timetable planning using hybrid particle swarm optimization
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Pipelining Memetic Algorithms, Constraint Satisfaction, and Local Search for Course Timetabling
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
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The timetabling problem consists of a set of subjects to be scheduled in different timeslots, a set of rooms in which the subjects can take place, a set of students who attend the subjects, and a set of subjects satisfied by rooms and required by timeslots. The heart of the problem is the constraints that exist as regulations within each resource and between resources. There are various solution approaches to solve the timetabling problem. This paper focuses on developing a constraint satisfaction problem model for a university timetabling problem. A solution of a constraint satisfaction problem is a consistent assignment of all variables to values in such a way that all constraints are satisfied. A sample case study problem is investigated and a constraint satisfaction programming approach is implemented using ILOG Scheduler and ILOG Solver. We use various goals in ILOG to investigate the performance of the CSP approach.