A Simulated Annealing Genetic Algorithm for Solving Timetable Problems

The post-enrolment course timetabling (PE-CTT) is one of the most studied timetabling problems, for which many instances and results are available. In this paper, we design a metaheuristic approach based on Simulated Annealing to solve the PE-CTT. We consider all the different variants of the problems that have been proposed in the literature and perform a comprehensive experimental analysis on all the public instances available. The outcome is that the solver, properly engineered and tuned, performs very well in all cases. Thus we provide the new best known results for many instances and state-of the-art values for the others. An algorithm SAGA for solving timetable problem was presented by analyzing all kinds of restricting conditions and special requirements in timetable arrangement of colleges and universities. Moreover, the crossover and mutation operators in the simulated annealing genetic algorithm were improved with adaptive strategy in order to enhance its searching ability and efficiency of the algorithm. The numerical experiments showed that the algorithm was efficient and feasible.