Open AccessPengoptimuman Penyelesaian Masalah Penjadualan Waktu Kuliah dengan Teknik Algoritma Genetik
Author(s) -
Fahrul Hakim Ayob,
Muhammad Sulaiman,
Mohd Fauzi Othman
Publication year - 2012
Publication title -
jurnal teknologi/jurnal teknologi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.191
H-Index - 22
eISSN - 2180-3722
pISSN - 0127-9696
DOI - 10.11113/jt.v40.409
Subject(s) - fitness function , computer science , genetic algorithm , artificial intelligence , humanities , mathematics , mathematics education , art , machine learning
Penjadualan waktu kuliah ialah suatu permasalahan penetapan masa dan tempat bagi sebilangan pengajaran kuliah. Penjadualan waktu perlu memastikan pematuhan beberapa kekangan yang disyaratkan seperti penghindaran pertindihan waktu mengajar, pertindihan subjek dan pertindihan bilik kuliah. Kertas kerja ini membincangkan tentang suatu simulasi penjadualan mudah yang telah dibina dan diskopkan untuk sesebuah fakulti kecil berteraskan konsep heuristik kepintaran buatan iaitu Algorikma Genetik dalam mencari penyelesaian optimumnya. Perwakilan penyelesaian masalah ini, maka kebarangkalian untuk penghasilan kromosom yang lebih cergas adalah sangat tinggi. Kecergasan Kromosom adalah adalah ukuran kualiti jadual waktu yang dihasilkan. Pada keseluruhannya, sistem ini berjaya memperlihatkan keberkesanan teknik Algoritma Genetik dalam pengoptimuman penyelesaian masalah penjadualan waktu tersebut. Kata Kunci: Algoritma genetik, penjadualan waktu kuliah, kromosom, mutasi, silang Lecture timetabling is the problem of assigning time and places for conducting lectures. Timetabling is required to satisfy a few constraints such as time of teaching, subjects, and places of conducting classes from clashes. This paper discusses the simulation of a simple lecture timetabling for a small faculty by using artificial intelligence’s heuristic method called Genetic Algoritms. The solution of the problem represented by the chromosomes would go through the processes of crossover and mutations at every generation.With the existence of a few functions which are called genetic repairs that support Genetic Algorithms in the problem optimization, the chances of getting healthy chromosomes is better. There functions enhance the chromosomes’ fitness function cost by reducing penalty cost from one generation to another. Fitness function of the chromosomes indicates the quality of timtable that is produced. As more fitness function is gained, a better quality timetable would be produced by this algorithm. Ultimately, this system has proved the effectiveness of Genetic Algorithms in optimising the timetabling problem.