| Issue |
ITM Web Conf.
Volume 45, 2022
2021 3rd International Conference on Computer Science Communication and Network Security (CSCNS2021)
|
|
|---|---|---|
| Article Number | 01033 | |
| Number of page(s) | 9 | |
| Section | Computer Technology and System Design | |
| DOI | https://doi.org/10.1051/itmconf/20224501033 | |
| Published online | 19 May 2022 | |
Whale optimization algorithm with opposition-based learning strategy for solving flexible job shop scheduling problem
1
Intelligent Electrical Science and Technology Research Institute, Northeastern University, Liaoning Shenyang, 110819, China
2
Key Laboratory of Integrated Energy Optimization and Secure Operation of Liaoning Province, Northeastern University, Liaoning, Shenyang, 110819, China
* Corresponding author: wuml_neu@163.com
Flexible job shop scheduling problem is the allocation of available shared resources and the sequencing of processing tasks within a certain period of time to meet certain or certain specific production indicators. The research and application of effective scheduling methods and optimization technologies are the foundation and key to realizing advanced manufacturing and improving production efficiency. Improving the production scheduling plan can greatly improve production efficiency and resource utilization, thereby enhancing the competitiveness of enterprises. Therefore, the production scheduling problem has always been a research hotspot in manufacturing systems. In this paper, we introduce the opposition-based learning strategy and combine it with whale optimization algorithm to solving flexible job shop scheduling problem better. 10 FJSP cases are introduced to test the performance of our algorithm and other comparison algorithms. The results obtrain show that our algorithm is more better and practical than other algorithm when dealing with FJSP cases.
Key words: Whale optimization algorithm / Flexible job shop scheduling problem / Makespan / Opposition-based learning strategy
© The Authors, published by EDP Sciences, 2022
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.