| Issue |
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
|
|
|---|---|---|
| Article Number | 00060 | |
| Number of page(s) | 7 | |
| DOI | https://doi.org/10.1051/bioconf/20249700060 | |
| Published online | 05 April 2024 | |
Evaluate Flexible Job Scheduling Problem Using Elitist Ant
1 College of Computer science and information technology, University of AL-Qadisiyah, AL-Qadisiyah, Iraq
2 University of AL-Qadisiyah, AL-Qadisiyah, Iraq
* Corresponding author: it.mast.23.8@qu.edu.iq
One significant approach that is frequently utilized in many applications, particularly in industry, is scheduling .An extension of job scheduling, the Flexible Job Shop Scheduling Problem (FJSP), has a number of real-world uses .This subject has received a lot of attention due to its significance and complexity .This research discusses the importance of scheduling in real-life operations, particularly in production scheduling. It presents a model using Elitist Ant (EAS) to compute the maximum value (makspane) of the flexible job shop scheduling problem(FJSP). The model programmed by Python ver. (3.) with two instance from Brandimarte dataset to optimize logistics costs and meet customer demand. The study deduce that there are two relations by using two new formula. Firstly, the relation between number of elitist ant and total ants . Also, the evaporate value is effected by the first relation directly.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.