Abstract
Examination Timetabling Problem is one of the optimization and combinatorial problems. It is proved to be a non-deterministic polynomial (NP)-hard problem. On a large scale of data, the examination timetabling problem becomes a complex problem and takes time if it solved manually. Therefore, heuristics exist to provide reasonable enough solutions and meet the constraints of the problem. In this study, a real-world dataset of Examination Timetabling (Toronto dataset) is solved using a Hill-Climbing and Tabu Search algorithm. Different from the approach in the literature, Tabu Search is a meta-heuristic method, but we implemented a Tabu Search within the hyper-heuristic framework. The main objective of this study is to provide a better understanding of the application of Hill-Climbing and Tabu Search in hyper-heuristics to solve timetabling problems. The results of the experiments show that Hill-Climbing and Tabu Search succeeded in automating the timetabling process by reducing the penalty 18-65% from the initial solution. Besides, we tested the algorithms within 10,000-100,000 iterations, and the results were compared with a previous study. Most of the solutions generated from this experiment are better compared to the previous study that also used Tabu Search algorithm.
Highlights
Examination Timetabling Problem is one of the optimization and combinatorial problems
The main objective of this study is to provide a better understanding of the application of Hill-Climbing and Tabu Search in hyper-heuristics to solve timetabling problems
The 3rd International Conference on course timetabling optimization using tabu-variable the Practice and Theory of Automated Timetabling Programme neighborhood search based hyper-heuristic algorithm
Summary
Peneliti menggunakan dua tipe struktur neighborhood yaitu single flipped dan block. Solusi yang diperoleh dari penelitian ini waktu yang cukup untuk persiapan antar ujian dan tidak juga dapat ditingkatkan dengan menggunakan metode mengharuskan siswa duduk di tiga ujian berturut-turt perbaikan lain seperti hyper-heuristic atau metadalam hari yang sama. Peneliti membandingkan hasil pengujian dengan 27 metode lain yang sudah pernah diterapkan dan dari hasilnya, algoritma β‐Hill Climbing memiliki penalti yang lebih baik pada satu dataset [10]. Adalah untuk melakukan pengujian beberapa algoritma Penelitian yang paling relevan dengan penelitian ini heuristik terhadap dataset Toronto untuk mendapatkan adalah penelitian yang dilakukan oleh Supoyo dkk solusi yang layak dan juga mendekati optimal dari (2019) dengan pendekatan hyper-heuristic dengan examination timetabling problem. Peneliti menggunakan metode yang berbeda dari beberapa penelitian sebelumnya yaitu, Hill-Climbing yang merupakan Local Search Heuristic dan Tabu Search yang merupakan meta-heuristic, namun peneliti menggunakan pendekatan hyperheuristic. Tre-s-92 Uta-s-92 Ute-s-92 Yor-f-83 merupakan kesimpulan dari penelitian yang sudah kami lakukan
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