| Autor: |
Wu Mingliang, Yang Dongsheng, Liu Tianyi |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
ITM Web of Conferences, Vol 45, p 01033 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2271-2097 |
| DOI: |
10.1051/itmconf/20224501033 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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