A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling

Abstract

The post enrolment course timetabling problem (PECTP) is one type of university course timetabling problems, in which a set of events has to be scheduled in time slots and located in suitable rooms according to the student enrolment data. The PECTP is an NP-hard combinatorial optimization problem and hence is very difficult to solve to optimality. This paper proposes a hybrid approach to solve the PECTP in two phases. In the first phase, a guided search genetic algorithm is applied to solve the PECTP. This guided search genetic algorithm, integrates a guided search strategy and some local search techniques, where the guided search strategy uses a data structure that stores useful information extracted from previous good individuals to guide the generation of offspring into the population and the local search techniques are used to improve the quality of individuals. In the second phase, a tabu search heuristic is further used on the best solution obtained by the first phase to improve the optimality of the solution if possible. The proposed hybrid approach is tested on a set of benchmark PECTPs taken from the international timetabling competition in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed hybrid approach is able to produce promising results for the test PECTPs.

Analysing the effects of solution space connectivity with an effective metaheuristic for the course timetabling problem

“…Nothegger et al also show that the results of their algorithm can be improved by adding a local search-based improvement method and by parallelising the algorithm. Jat and Yang [25] have also used a weighted sum objective function in their hybrid evolutionary algorithm/tabu search approach, though their results do not appear as strong as those of the previous two papers. Similarly, van den Broek and Hurkens [47] have also used a weighted sum objective function in their deterministic algorithm based on column generation techniques.…”

Iterated local search using an add and delete hyper-heuristic for university course timetabling

“…In Jat et al [46] a two phase approach is used. In the first phase, a guided genetic algorithm is applied which integrates local search.…”

Effective learning hyper-heuristics for the course timetabling problem

“…The algorithm obtains good results on the ITC-2007 instances, but it is not superior to the top entries, and fails to produce new best-known solutions. Jat and Yang (2011). Use a hybrid population-based algorithm of 2 phases.…”