Research on low carbon welding scheduling based on production process
Abstract The welding workshop of metal structural parts is highly energy-consuming. To meet the national low-carbon green demand, this paper focus on the welding workshop scheduling problem in production process with considering the carbon footprints such as equipment energy consumption, welding mat...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-79555-0 |
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| _version_ | 1846158601678225408 |
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| author | Rong Hua Meng Zan Yang Wang Wen Hui Zeng Feng Guan Ding Kun Lei Zheng Jia Wu Shao Hua Deng |
| author_facet | Rong Hua Meng Zan Yang Wang Wen Hui Zeng Feng Guan Ding Kun Lei Zheng Jia Wu Shao Hua Deng |
| author_sort | Rong Hua Meng |
| collection | DOAJ |
| description | Abstract The welding workshop of metal structural parts is highly energy-consuming. To meet the national low-carbon green demand, this paper focus on the welding workshop scheduling problem in production process with considering the carbon footprints such as equipment energy consumption, welding material consumption and shielding gas consumption. Firstly, a bi-objective low-carbon welding scheduling mathematical model is established with minimizing makespan and carbon emission. Then, an improved Grey Wolf Optimizer (IGWO) with three strategies is designed to solve this multi-objective problem. The grey wolf multi-wandering strategy (first) is proposed to enhance the population diversity. The grey wolf coordinated hunting strategy (second) based on dynamic weights is introduced to improve the convergence of IGWO. A local optimization strategy(third) is designed to improve the post-optimal search performance by adjusting the machine assignment based on the critical path. A welding workshop green scheduling case is designed to verify the model and algorithm proposed in this paper. The minimum completion time and carbon emissions obtained by the IGWO algorithm are 842.14 and 3.85E + 05, respectively. This result is better than that obtained by NSGA-II and GWO.The results show that the model effectively reduce the carbon emissions of the workshop, and the algorithm can effectively solve the model. |
| format | Article |
| id | doaj-art-7d1f55e42a334dfb8dc8f27ff54fa0e4 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7d1f55e42a334dfb8dc8f27ff54fa0e42024-11-24T12:22:27ZengNature PortfolioScientific Reports2045-23222024-11-0114111610.1038/s41598-024-79555-0Research on low carbon welding scheduling based on production processRong Hua Meng0Zan Yang Wang1Wen Hui Zeng2Feng Guan3Ding Kun Lei4Zheng Jia Wu5Shao Hua Deng6Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges UniversityHubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges UniversityHubei Provincial Engineering Research Center of Robotics & Intelligent Manufacturing, Wuhan University of TechnologyHubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges UniversityChina Three Gorges Materials and Tendering Management Co., Ltd.Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges UniversityHubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges UniversityAbstract The welding workshop of metal structural parts is highly energy-consuming. To meet the national low-carbon green demand, this paper focus on the welding workshop scheduling problem in production process with considering the carbon footprints such as equipment energy consumption, welding material consumption and shielding gas consumption. Firstly, a bi-objective low-carbon welding scheduling mathematical model is established with minimizing makespan and carbon emission. Then, an improved Grey Wolf Optimizer (IGWO) with three strategies is designed to solve this multi-objective problem. The grey wolf multi-wandering strategy (first) is proposed to enhance the population diversity. The grey wolf coordinated hunting strategy (second) based on dynamic weights is introduced to improve the convergence of IGWO. A local optimization strategy(third) is designed to improve the post-optimal search performance by adjusting the machine assignment based on the critical path. A welding workshop green scheduling case is designed to verify the model and algorithm proposed in this paper. The minimum completion time and carbon emissions obtained by the IGWO algorithm are 842.14 and 3.85E + 05, respectively. This result is better than that obtained by NSGA-II and GWO.The results show that the model effectively reduce the carbon emissions of the workshop, and the algorithm can effectively solve the model.https://doi.org/10.1038/s41598-024-79555-0Low carbon welding schedulingGrey Wolf OptimizerCarbon emissionsMulti-objective optimization |
| spellingShingle | Rong Hua Meng Zan Yang Wang Wen Hui Zeng Feng Guan Ding Kun Lei Zheng Jia Wu Shao Hua Deng Research on low carbon welding scheduling based on production process Scientific Reports Low carbon welding scheduling Grey Wolf Optimizer Carbon emissions Multi-objective optimization |
| title | Research on low carbon welding scheduling based on production process |
| title_full | Research on low carbon welding scheduling based on production process |
| title_fullStr | Research on low carbon welding scheduling based on production process |
| title_full_unstemmed | Research on low carbon welding scheduling based on production process |
| title_short | Research on low carbon welding scheduling based on production process |
| title_sort | research on low carbon welding scheduling based on production process |
| topic | Low carbon welding scheduling Grey Wolf Optimizer Carbon emissions Multi-objective optimization |
| url | https://doi.org/10.1038/s41598-024-79555-0 |
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