Open AccessSubject Scheduling Using Genetic Algorithms (Case Study: SMK Negeri 1 Labang-Madura-Indonesia)
Author(s) -
Eka Mala Sari Rochman,
Muhammad Ali Syakur,
Imamah Imamah,
Aeri Rachmad
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1569/2/022074
Subject(s) - computer science , schedule , scheduling (production processes) , mathematical optimization , genetic algorithm , population , job shop scheduling , machine learning , mathematics , medicine , operating system , environmental health
The manually scheduling system is considered less effective and efficient because it requires a long time. Problems will become more complex if the number of components or data used is increasing. The schedule is expected not only not to experience clashes, but also to adjust to some limitations that must be met. Genetic Algorithm is one of the heuristic search algorithms that are very well used in solving optimization problems. The problem of scheduling genetic algorithms is considered to have good performance in finding the optimal solution. Genetic Algorithms implement an evolutionary process by randomly producing chromosomes from each population These chromosomes produce a solution to the problem raised, namely scheduling subjects. The conclusion of this study is to be able to arrange the schedule of subjects efficiently, by overcoming obstacles such as clashing schedules without eliminating the constraints that must be met.