Great Deluge Based Hyper-heuristics for Solving Real-world University Examination Timetabling Problem: New Data set and Approach

Abstract

The university examination timetabling problem is one of challenging optimization problems. Its NP-hard nature makes this problem attractive to be studied, especially in the field of operation research and artificial intelligence. In the literature, the state-of-the-art approach for solving examination timetabling problem is meta-heuristics. However, this approach has limitation, i.e. the need for intensive problem-specific parameter tuning. To cope with this problem, a relatively new approach namely hyper-heuristics was proposed. Different from meta-heuristics that search upon solution space, hyper-heuristics search upon low-level space. This strategy makes hyper-heuristics more generic that works over cross-domain, compared to meta-heuristics that usually designed for specific problem domain. This paper reports the success of solving real-world university examination timetabling problem in Institut Teknologi Sepuluh Nopember using hyper-heuristics based on great deluge algorithm. The main contributions of this study are two folds: a new dataset and new approach for solving examination timetabling problem. The computational results show that the proposed algorithm could produce much better solutions compared to the solutions generated manually. In addition, the proposed algorithm also outperforms two benchmarking algorithms, namely hill climbing and simulated annealing algorithms.