SCHEDULING USING GENETIC ALGORITHM AND ROULETTE WHEEL SELECTION METHOD CONSIDERING LECTURER TIME
DOI:
https://doi.org/10.30818/jitu.2.1.2243Abstract
Scheduling lectures is not something easy, considering many factors that must be considered. The factors that must be considered are the courses that will be held, the space available, the lecturers, the suitability of the credits with the duration of courses, the availability of lecturers' time, and so on. One algorithm in the field of computer science that can be used in lecture scheduling automation is Genetic Algorithms. Genetic Algorithms can provide the best solution from several solutions in handling scheduling problems and the selksi method used is roulette wheel. This study produces a scheduling system that can work automatically or independently which can produce optimal lecture schedules by applying Genetic Algorithms. Based on the results of testing, the resulting system can schedule lectures correctly and consider the time of lecturers. In this study, the roulette wheel selection method was more effective in producing the best individuals than the rank selection method.References
Soyemi J., Akinode J., Oloruntoba S., 2017. Electronic Lecture Time-table Scheduler Using Genetic Algorithm., IEEE 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress., pp:710-715
Herman., Lukman Syafie., Dirgahayu Lantara., 2017. Lecture Scheduling Automation Using Genetic Algorithm, East Indonesia Conference on Computer and Information Technology (EIConCIT), pp: 23-27., Balikpapan, Indonesia.
Peng C., Fang Y., Lou P., Yan J., 2018. Analysis of Double-Resource Flexible Job Shop Scheduling Problem Based on Genetic Algorithm, 15th International Conference on Networking, Sensing and Control (ICNSC), pp:1-6., Zhuhai, China.
Kurniawan B., Gozali A.A., Weng W., Fujimura S., 2017. A Genetic Algorithm for Unrelated Parallel Machine Scheduling Minimizing Makespan Cost and Electricity Cost Under Time-of-Use (TOU) Tariffs with Job Delay Mechanism, IEEE International Cenference on Industrial Engineering and Engineering Management (IEEM), pp:583-587. Singapore.
Lu H and Qiao F., 2017. An Improved Genetic Algorithm for a Parallel Machine Shceduling Problem with Energy Consideration, 13th IEEE Conference on Automation Science and Engineering (CASE), pp: 1487-1492., Xi’an, China.
Zuo X, “Vehicle Scheduling of an Urban Bus Line via an Improved Multiobjective Genetic Algorithm”. IEEE Transaction on Intelligent Transportation System, Vol 16, No. 2, pp:1030-1041, 2015
Downloads
Published
Issue
Section
License
The proposed policy for journals that offer open access
Authors who publish with this journal agree to the following terms:
- Copyright on any article is retained by the author(s).
- Author grant the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
- The article and any associated published material is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
