The post enrolment based course timetabling problem (PECTP) is one type of university course timetabling problem which a set of events has to be assigned into time slots and suitable rooms according to students' enrolment data. This problem is classified as a combinatorial optimization problem and it is very hard to solve the problem efficiently because solving the problem is to find an optimal timetable which it must satisfy all hard constraints and should satisfy soft constraints as much as possible. Moreover, this problem is technically complicated and highly time-consuming and it is known to be in the NP-complete class. In this paper we have developed a genetic algorithm hybridized with a local search technique and a tabu search heuristic for solving the PECTP. The algorithm takes advantage of the exploitation ability of a local search technique and a tabu search heuristic to improve the results obtained in the exploration phase of the genetic algorithm. In addition, the proposed hybrid approach was tested on a set of standard benchmark problem in comparison with other methods from the literature, and experimental results show that the proposed hybrid approach was able to find promising solutions for solving the PECTP.
The post enrolment based course timetabling problem (PECTP) is one type of university course timetabling problem which a set of events has to be scheduled into time slots and suitable rooms according to students' enrolment data. This problem is classified as a NP-complete combinatorial optimization problem and hence it is very hard and highly time-consuming to solve the problem to find an optimal timetable. In this paper we have developed a non-dominated sorting genetic algorithm (NSGA-II) hybridized with two local search technique and a tabu search heuristic for solving the multi-objective PECTP. In addition to the original LS technique will be used in NSGA-II, a new LS technique is also used in NSGA-II in order to further improve the performance of NSGA-II, especially reducing violation of the first soft constraint. The algorithm takes advantage of the exploitation ability of two local search techniques and a tabu search heuristic to improve the results obtained in the exploration phase of the NSGA-II. The proposed hybrid approach is tested on a set of standard benchmark problem in comparison with other methods from the literature, and experimental results show that the proposed hybrid approach is able to find promising solutions for solving the multi-objective PECTP.
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