Solving Examination Timetabling Problem using Partial Exam Assignment with Great Deluge Algorithm

Constructing a quality solution for the examination timetable problem is a difficult task. This paper presents a partial exam assignment approach with great deluge algorithm as the improvement mechanism in order to generate good quality timetable. In this approach, exams are ordered based on graph heuristics and only selected exams (partial exams) are scheduled first and then improved using great deluge algorithm. The entire process continues until all of the exams have been scheduled. We implement the proposed technique on the Toronto benchmark datasets. Experimental results indicate that in all problem instances, this proposed method outperforms traditional great deluge algorithm and when comparing with the state-of-the-art approaches, our approach produces competitive solution for all instances, with some cases outperform other reported result

An evolutionary non-Linear great deluge approach for solving course timetabling problems

The aim of this paper is to extend our non-linear great deluge algorithm into an evolutionary approach by incorporating a population and a mutation operator to solve the university course timetabling problems. This approach might be seen as a variation of memetic algorithms. The popularity of evolutionary computation approaches has increased and become an important technique in solving complex combinatorial optimisation problems. The proposed approach is an extension of a non-linear great deluge algorithm in which evolutionary operators are incorporated. First, we generate a population of feasible solutions using a tailored process that incorporates heuristics for graph colouring and assignment problems. The initialisation process is capable of producing feasible solutions even for large and most constrained problem instances. Then, the population of feasible timetables is subject to a steady-state evolutionary process that combines mutation and stochastic local search. We conducted experiments to evaluate the performance of the proposed algorithm and in particular, the contribution of the evolutionary operators. The results showed the effectiveness of the hybridisation between non-linear great deluge and evolutionary operators in solving university course timetabling problems

An evolutionary non-Linear great deluge approach for solving course timetabling problems

The aim of this paper is to extend our non-linear great deluge algorithm into an evolutionary approach by incorporating a population and a mutation operator to solve the university course timetabling problems. This approach might be seen as a variation of memetic algorithms. The popularity of evolutionary computation approaches has increased and become an important technique in solving complex combinatorial optimisation problems. The proposed approach is an extension of a non-linear great deluge algorithm in which evolutionary operators are incorporated. First, we generate a population of feasible solutions using a tailored process that incorporates heuristics for graph colouring and assignment problems. The initialisation process is capable of producing feasible solutions even for large and most constrained problem instances. Then, the population of feasible timetables is subject to a steady-state evolutionary process that combines mutation and stochastic local search. We conducted experiments to evaluate the performance of the proposed algorithm and in particular, the contribution of the evolutionary operators. The results showed the effectiveness of the hybridisation between non-linear great deluge and evolutionary operators in solving university course timetabling problems

Non-linear great deluge with learning mechanism for solving the course timetabling problem

International audienc

IMPLEMENTASI METODE GREAT DELUGE HYPER-HEURISTIC PADA PERMASALAHAN PENJADWALAN UJIAN

Penelitian ini membahas tentang penjadwalan ujian menggunakan metode Great Deluge Hyper Heuristic dengan Bahasa pemrograman Pyhton 3.10. Terdapat dua tahap untuk menjadwalkan ujian, yaitu pembentukan solusi awal menggunakan Graph Colouring dan perbaikan solusi dengan Algoritma Great Deluge Hyper-Heuristic. Solusi awal yang telah dihasilkan algoritma Graph Colouring diubah untuk menemukan jadwal yang lebih baik dengan menggunakan dua pendekatan low level heuristic. Pada setiap iterasi, akan ada pemilihan low level heuristic yang darinya menghasilkan kandidat jadwal baru. Kemudian nilai proximity kandidat jadwal baru akan dibandingkan dengan nilai proximity jadwal sebelumnya. Apabila nilai proximity kandidat jadwal baru kurang dari nilai proximity jadwal terbaik sebelumnya atau kurang dari level, maka jadwal tersebut diterima. Solusi optimal akan diperoleh dari iterasi terakhir algoritma Great Deluge Hyper-Heuristic. Hasil implementasi metode Great Deluge Hyper-Heuristic untuk masalah penjadwalan ujian di York Mills Collegiate Institute menunjukkan bahwa metode Great Deluge Hyper-Heuristic dapat menyelesaikan masalah penjadwalan ujian dan dapat menghasilkan jadwal yang sebagaian besar memenuhi soft constrains

Non-linear great deluge with reinforcement learning for university course timetabling

This paper describes a non-linear great deluge hyper-heuristic incorporating a reinforcement learning mechanism for the selection of low-level heuristics and a non-linear great deluge acceptance criterion. The proposed hyper-heuristic deals with complete solutions, i.e. it is a solution improvement approach not a constructive one. Two types of reinforcement learning are investigated: learning with static memory length and learning with dynamic memory length. The performance of the proposed algorithm is assessed using eleven test instances of the university course timetabling problem. The experimental results show that the non-linear great deluge hyper-heuristic performs better when using static memory than when using dynamic memory. Furthermore, the algorithm with static memory produced new best results for ?ve of the test instances while the algorithm with dynamic memory produced four best results compared to the best known results from the literature