Self Adaptive and Simulated Annealing Hyper-Heuristics Approach for Post-Enrollment Course Timetabling

One of timetabling problem in education field is Post-Enrollment Course Timetabling (PE-CTT). The challenges faced in the PE-CTT are differences in type of problems, a number of limitations, and requirements that differ from one university to another. It is difficult to find common and effective solutions. State-of-art method that can develop more general systems by using cheaper methods and still being able to solve problems is Hyper-Heuristic approach. The Self-Adaptive Strategy is used as a strategy for selecting Low-Level-Heuristics (LLH) and Simulated Annealing as a Move Acceptance (MA) strategy to solve the course timetabling problems. Self-adaptive can increase the level of convergence to the optimal value in the optimization process. Simulated Annealing can accept solutions that are no better so that the final solution is not trapped in the local optima solution. The contributions of this paper are state-of-art hybridization of Self-Adaptive and Simulated Annealing Hyper-Heuristic approach to solve post-enrollment course timetabling problem. The Self Adaptive and Simulated Annealing Hyper-Heuristics then compared with Simple Random and Simulated Annealing Hyper-Heuristics. Tests will be carried out on Socha dataset. The hybridization of Self Adaptive and Simulated Annealing shows competitive results.