Scheduling Method of AGV in Spinning Workshop Based on Simulated Annealing Ant Colony Optimization
Focusing on the lack of effective methods for collaborative scheduling of AGV (Automated Guided Vehicle) in ring spinning workshops. Under various constraints covering point demand constraint, path flow constraint, AGV capacity constraint, starting point constraint and variable constraint, an AGV sc...
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| Format: | Article |
| Language: | English |
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Tamkang University Press
2025-06-01
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| Series: | Journal of Applied Science and Engineering |
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| Online Access: | http://jase.tku.edu.tw/articles/jase-202601-29-01-0002 |
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| author | Jinglong Xiong Yiping Yuan Yongsheng Chao Ming Li Adilanmu Sitahong Peiyin Mo |
| author_facet | Jinglong Xiong Yiping Yuan Yongsheng Chao Ming Li Adilanmu Sitahong Peiyin Mo |
| author_sort | Jinglong Xiong |
| collection | DOAJ |
| description | Focusing on the lack of effective methods for collaborative scheduling of AGV (Automated Guided Vehicle) in ring spinning workshops. Under various constraints covering point demand constraint, path flow constraint, AGV capacity constraint, starting point constraint and variable constraint, an AGV scheduling model for spinning workshops is constructed. The model aims to minimize AGV moving distance and the maximum completion time. A model solution method based on simulated annealing ant colony optimization (SAACO) is
proposed. SAACO combines the advantages of Simulated Annealing Algorithm (SA) and Ant Colony Algorithm (ACO), SAACO is not easy to fall into the local optimal solution and has higher model solving efficiency. The simulation of actual data shows that when the number of cans is 60 , respectively compared with the traditional SA and ACO, the SAACO method reduces the total distance of AGV movement by 34.83 m and 24.13 m , the maximum completion time by 15 s and 13 s , the algorithm running time by 20% − 30%. This approach can
reduce the operational costs of AGV in spinning workshops, enhance workshop efficiency, and provide a novel solution for the cooperative scheduling of AGV in such settings. |
| format | Article |
| id | doaj-art-b51cf8a3e48c4acb9618be8bc78516f6 |
| institution | Matheson Library |
| issn | 2708-9967 2708-9975 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Tamkang University Press |
| record_format | Article |
| series | Journal of Applied Science and Engineering |
| spelling | doaj-art-b51cf8a3e48c4acb9618be8bc78516f62025-06-25T11:02:11ZengTamkang University PressJournal of Applied Science and Engineering2708-99672708-99752025-06-01291132210.6180/jase.202601_29(1).0002Scheduling Method of AGV in Spinning Workshop Based on Simulated Annealing Ant Colony OptimizationJinglong Xiong0Yiping Yuan1Yongsheng Chao2Ming Li3Adilanmu Sitahong4Peiyin Mo5Intelligent Manufacturing Modern Industry College, Xinjiang University, Urumqi, 830017, ChinaIntelligent Manufacturing Modern Industry College, Xinjiang University, Urumqi, 830017, ChinaIntelligent Manufacturing Modern Industry College, Xinjiang University, Urumqi, 830017, ChinaIntelligent Manufacturing Modern Industry College, Xinjiang University, Urumqi, 830017, ChinaIntelligent Manufacturing Modern Industry College, Xinjiang University, Urumqi, 830017, ChinaIntelligent Manufacturing Modern Industry College, Xinjiang University, Urumqi, 830017, ChinaFocusing on the lack of effective methods for collaborative scheduling of AGV (Automated Guided Vehicle) in ring spinning workshops. Under various constraints covering point demand constraint, path flow constraint, AGV capacity constraint, starting point constraint and variable constraint, an AGV scheduling model for spinning workshops is constructed. The model aims to minimize AGV moving distance and the maximum completion time. A model solution method based on simulated annealing ant colony optimization (SAACO) is proposed. SAACO combines the advantages of Simulated Annealing Algorithm (SA) and Ant Colony Algorithm (ACO), SAACO is not easy to fall into the local optimal solution and has higher model solving efficiency. The simulation of actual data shows that when the number of cans is 60 , respectively compared with the traditional SA and ACO, the SAACO method reduces the total distance of AGV movement by 34.83 m and 24.13 m , the maximum completion time by 15 s and 13 s , the algorithm running time by 20% − 30%. This approach can reduce the operational costs of AGV in spinning workshops, enhance workshop efficiency, and provide a novel solution for the cooperative scheduling of AGV in such settings.http://jase.tku.edu.tw/articles/jase-202601-29-01-0002spinning workshopcollaborative schedulingsimulated annealing ant colony optimizationautomated guided vehiclepath planningoptimal path |
| spellingShingle | Jinglong Xiong Yiping Yuan Yongsheng Chao Ming Li Adilanmu Sitahong Peiyin Mo Scheduling Method of AGV in Spinning Workshop Based on Simulated Annealing Ant Colony Optimization Journal of Applied Science and Engineering spinning workshop collaborative scheduling simulated annealing ant colony optimization automated guided vehicle path planning optimal path |
| title | Scheduling Method of AGV in Spinning Workshop Based on Simulated Annealing Ant Colony Optimization |
| title_full | Scheduling Method of AGV in Spinning Workshop Based on Simulated Annealing Ant Colony Optimization |
| title_fullStr | Scheduling Method of AGV in Spinning Workshop Based on Simulated Annealing Ant Colony Optimization |
| title_full_unstemmed | Scheduling Method of AGV in Spinning Workshop Based on Simulated Annealing Ant Colony Optimization |
| title_short | Scheduling Method of AGV in Spinning Workshop Based on Simulated Annealing Ant Colony Optimization |
| title_sort | scheduling method of agv in spinning workshop based on simulated annealing ant colony optimization |
| topic | spinning workshop collaborative scheduling simulated annealing ant colony optimization automated guided vehicle path planning optimal path |
| url | http://jase.tku.edu.tw/articles/jase-202601-29-01-0002 |
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