Whale optimization algorithm with opposition-based learning strategy for solving flexible job shop scheduling problem
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 technol...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2022-01-01
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| Series: | ITM Web of Conferences |
| Subjects: | |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2022/05/itmconf_cscns2022_01033.pdf |
| _version_ | 1811232466524962816 |
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| author | Wu Mingliang Yang Dongsheng Liu Tianyi |
| author_facet | Wu Mingliang Yang Dongsheng Liu Tianyi |
| author_sort | Wu Mingliang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T11:04:18Z |
| format | Article |
| id | doaj.art-4027b83dac4e4367aac7352f3ff11351 |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-04-12T11:04:18Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-4027b83dac4e4367aac7352f3ff113512022-12-22T03:35:51ZengEDP SciencesITM Web of Conferences2271-20972022-01-01450103310.1051/itmconf/20224501033itmconf_cscns2022_01033Whale optimization algorithm with opposition-based learning strategy for solving flexible job shop scheduling problemWu Mingliang0Yang Dongsheng1Liu Tianyi2Intelligent Electrical Science and Technology Research Institute, Northeastern UniversityIntelligent Electrical Science and Technology Research Institute, Northeastern UniversityIntelligent Electrical Science and Technology Research Institute, Northeastern UniversityFlexible 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.https://www.itm-conferences.org/articles/itmconf/pdf/2022/05/itmconf_cscns2022_01033.pdfwhale optimization algorithmflexible job shop scheduling problemmakespanopposition-based learning strategy |
| spellingShingle | Wu Mingliang Yang Dongsheng Liu Tianyi Whale optimization algorithm with opposition-based learning strategy for solving flexible job shop scheduling problem ITM Web of Conferences whale optimization algorithm flexible job shop scheduling problem makespan opposition-based learning strategy |
| title | Whale optimization algorithm with opposition-based learning strategy for solving flexible job shop scheduling problem |
| title_full | Whale optimization algorithm with opposition-based learning strategy for solving flexible job shop scheduling problem |
| title_fullStr | Whale optimization algorithm with opposition-based learning strategy for solving flexible job shop scheduling problem |
| title_full_unstemmed | Whale optimization algorithm with opposition-based learning strategy for solving flexible job shop scheduling problem |
| title_short | Whale optimization algorithm with opposition-based learning strategy for solving flexible job shop scheduling problem |
| title_sort | whale optimization algorithm with opposition based learning strategy for solving flexible job shop scheduling problem |
| topic | whale optimization algorithm flexible job shop scheduling problem makespan opposition-based learning strategy |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2022/05/itmconf_cscns2022_01033.pdf |
| work_keys_str_mv | AT wumingliang whaleoptimizationalgorithmwithoppositionbasedlearningstrategyforsolvingflexiblejobshopschedulingproblem AT yangdongsheng whaleoptimizationalgorithmwithoppositionbasedlearningstrategyforsolvingflexiblejobshopschedulingproblem AT liutianyi whaleoptimizationalgorithmwithoppositionbasedlearningstrategyforsolvingflexiblejobshopschedulingproblem |