Open AccessTeam-Teaching-Based Course Scheduling Using Genetic Algorithm
Author(s) -
Rafika Sari,
Khairunnisa Fadhilla Ramdhania,
Rakhmat Purnomo
Publication year - 2022
Publication title -
penelitian ilmu komputer sistem embedded and logic/penelitian ilmu komputer sistem embedded and logic
Language(s) - English
Resource type - Journals
eISSN - 2620-3553
pISSN - 2303-3304
DOI - 10.33558/piksel.v10i1.4416
Subject(s) - workload , scheduling (production processes) , genetic algorithm , computer science , fitness function , population , job shop scheduling , mathematics education , mathematical optimization , machine learning , mathematics , medicine , schedule , environmental health , operating system
Scheduling problems occur in various fields, e.g., education, health institutions, transportation, sports, etc. Main scheduling problems in education is course scheduling which creates schedules for students and lecturers. In this study, course scheduling allocates the lecturers in the form of team teaching and courses into the class and a certain time to even out the workload of lecturers per day and a group of students per day in one week without breaking the constraint. The method used in this research is a genetic algorithm where Universitas Bhayangkara Jakarta Raya as the case study. The genetic algorithm process is done by getting several candidate solutions that undergo a process of selection, mutation, and crossing over to produce chromosomes with the best fitness values. The objective function in this research is minimizing the average variance of the workload of lecturers and students per day in one week. The parameters used in genetic algorithm are determined based on the Design of Experiments mechanism (DOE). The optimal parameter values used to run the program are as: population size = 50, with probability of crossing over = 0.4 and probability of mutation = 0.008. The results of scheduling with genetic algorithms show that the value of the workload variance lecturers and students by considering team teaching is better than actual scheduling. The application of the genetic algorithm method results in a decrease in the standard value deviation of the workload of lecturers and a group of students in one week is 0.114 (3.68%) and 3.11 (55.7%). In addition, course scheduling uses a genetic algorithm with consider team teaching better than genetic algorithm without considering team teaching because there is no class schedule that clashes in real conditions.