Multi-AGV multitask collaborative scheduling based on an improved ant colony algorithm
Research on multitask scheduling systems in factory environments is a popular topic in the field of intelligent manufacturing. Existing research mainly focuses on the optimization of automated guided vehicle (AGV) path planning and scheduling, emphasizing on the minimization of conflicts and deadloc...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-01-01
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| Series: | International Journal of Advanced Robotic Systems |
| Online Access: | https://doi.org/10.1177/17298806241312784 |
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| _version_ | 1832540437799239680 |
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| author | Yazhen Zhu Qing Song Meng Li |
| author_facet | Yazhen Zhu Qing Song Meng Li |
| author_sort | Yazhen Zhu |
| collection | DOAJ |
| description | Research on multitask scheduling systems in factory environments is a popular topic in the field of intelligent manufacturing. Existing research mainly focuses on the optimization of automated guided vehicle (AGV) path planning and scheduling, emphasizing on the minimization of conflicts and deadlocks, multi-objective task scheduling, and metaheuristic algorithm optimization, while ignoring path stability and real-time path planning in dynamic environments. Therefore, this paper aims to address these issues to better handle dynamic changes in actual operating environments. This paper establishes a mathematical model with the optimization objective of minimizing the overall running time of material distribution tasks and proposes an improved ant colony algorithm to optimize the model. First, the concept of prior time is introduced to improve the traditional ant colony algorithm. The path of the ongoing task is introduced with a time calculation, and the occupancy time window of each grid point on the path is calculated. Based on this, the initial pheromone distribution on subsequent paths is altered dynamically, which accelerates the convergence of the ants to a collision-free path. Second, in the pheromone update stage, the method of calculating the pheromone increment in the traditional ant colony algorithm is modified. The original distance influence factor is changed to a time influence factor, which ensures that all tasks still have the minimum running time when calculating a collision-free path. Finally, through 30 sets of simulation experiments on material distribution tasks, it is shown that the proposed algorithm shortens the total running time by 15.14%, 12.87%, and 10.59% compared to two ant colony algorithms and one strategic multi-AGV scheduling algorithm, respectively, thus verifying the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-0ab261f1b02e4024b81436301429df85 |
| institution | Kabale University |
| issn | 1729-8814 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | International Journal of Advanced Robotic Systems |
| spelling | doaj-art-0ab261f1b02e4024b81436301429df852025-02-05T02:03:24ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142025-01-012210.1177/17298806241312784Multi-AGV multitask collaborative scheduling based on an improved ant colony algorithmYazhen ZhuQing SongMeng LiResearch on multitask scheduling systems in factory environments is a popular topic in the field of intelligent manufacturing. Existing research mainly focuses on the optimization of automated guided vehicle (AGV) path planning and scheduling, emphasizing on the minimization of conflicts and deadlocks, multi-objective task scheduling, and metaheuristic algorithm optimization, while ignoring path stability and real-time path planning in dynamic environments. Therefore, this paper aims to address these issues to better handle dynamic changes in actual operating environments. This paper establishes a mathematical model with the optimization objective of minimizing the overall running time of material distribution tasks and proposes an improved ant colony algorithm to optimize the model. First, the concept of prior time is introduced to improve the traditional ant colony algorithm. The path of the ongoing task is introduced with a time calculation, and the occupancy time window of each grid point on the path is calculated. Based on this, the initial pheromone distribution on subsequent paths is altered dynamically, which accelerates the convergence of the ants to a collision-free path. Second, in the pheromone update stage, the method of calculating the pheromone increment in the traditional ant colony algorithm is modified. The original distance influence factor is changed to a time influence factor, which ensures that all tasks still have the minimum running time when calculating a collision-free path. Finally, through 30 sets of simulation experiments on material distribution tasks, it is shown that the proposed algorithm shortens the total running time by 15.14%, 12.87%, and 10.59% compared to two ant colony algorithms and one strategic multi-AGV scheduling algorithm, respectively, thus verifying the effectiveness of the proposed method.https://doi.org/10.1177/17298806241312784 |
| spellingShingle | Yazhen Zhu Qing Song Meng Li Multi-AGV multitask collaborative scheduling based on an improved ant colony algorithm International Journal of Advanced Robotic Systems |
| title | Multi-AGV multitask collaborative scheduling based on an improved ant colony algorithm |
| title_full | Multi-AGV multitask collaborative scheduling based on an improved ant colony algorithm |
| title_fullStr | Multi-AGV multitask collaborative scheduling based on an improved ant colony algorithm |
| title_full_unstemmed | Multi-AGV multitask collaborative scheduling based on an improved ant colony algorithm |
| title_short | Multi-AGV multitask collaborative scheduling based on an improved ant colony algorithm |
| title_sort | multi agv multitask collaborative scheduling based on an improved ant colony algorithm |
| url | https://doi.org/10.1177/17298806241312784 |
| work_keys_str_mv | AT yazhenzhu multiagvmultitaskcollaborativeschedulingbasedonanimprovedantcolonyalgorithm AT qingsong multiagvmultitaskcollaborativeschedulingbasedonanimprovedantcolonyalgorithm AT mengli multiagvmultitaskcollaborativeschedulingbasedonanimprovedantcolonyalgorithm |