Grid map model of the factory area.
<div><p>Research on task allocation for multiple automated guided vehicles (AGVs) in factory environments is a key topic in intelligent manufacturing. Existing studies often struggle to balance fairness and priority in task allocation, leading to low AGV utilization and high no-load dist...
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| مؤلفون آخرون: | , |
| منشور في: |
2025
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| _version_ | 1852019750876479488 |
|---|---|
| author | Yazhen Zhu (3595430) |
| author2 | Qing Song (77132) Meng Li (79487) |
| author2_role | author author |
| author_facet | Yazhen Zhu (3595430) Qing Song (77132) Meng Li (79487) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yazhen Zhu (3595430) Qing Song (77132) Meng Li (79487) |
| dc.date.none.fl_str_mv | 2025-06-02T18:18:05Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0321616.g001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Grid_map_model_of_the_factory_area_/29217526 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Ecology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> research material delivery tasks low agv utilization extensive simulation experiments enhancing agv utilization population genetic algorithm task allocation algorithms guide multiple agvs real factory environment task allocation factory environments proposed algorithm subsequently enhanced slightly higher significantly lower running time overall performance load distances key topic intelligent manufacturing conducted based balance fairness also optimizing |
| dc.title.none.fl_str_mv | Grid map model of the factory area. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Research on task allocation for multiple automated guided vehicles (AGVs) in factory environments is a key topic in intelligent manufacturing. Existing studies often struggle to balance fairness and priority in task allocation, leading to low AGV utilization and high no-load distances. Moreover, the stability and applicability of task allocation algorithms in real-world production environments face significant challenges. To address these issues, a mathematical model is formulated with the objective of minimizing the no-load distances of all AGVs in material delivery tasks. The model is subsequently enhanced by incorporating task allocation balance and priority. To solve the optimization model, an improved particle swarm optimization algorithm is proposed, and extensive simulation experiments are conducted based on a real factory environment. By comparing the optimization results of the proposed algorithm with those of the latest multi-population genetic algorithm (MGA) and the market-based bundle task allocation method (MBTA), it is evident that both the proposed algorithm and MGA achieve higher AGV utilization and shorter total task completion times than MBTA, while also optimizing no-load distances. Although the running time of the proposed algorithm is slightly higher than that of MBTA, it is significantly lower than that of MGA, and its overall performance in reducing no-load distances and enhancing AGV utilization is superior to that of MGA. The proposed method can be applied to guide multiple AGVs in multi-material delivery tasks in real factory environments.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_d48779bf653e893d06df0d81058859a4 |
| identifier_str_mv | 10.1371/journal.pone.0321616.g001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29217526 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Grid map model of the factory area.Yazhen Zhu (3595430)Qing Song (77132)Meng Li (79487)EcologySpace ScienceBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedxlink "> researchmaterial delivery taskslow agv utilizationextensive simulation experimentsenhancing agv utilizationpopulation genetic algorithmtask allocation algorithmsguide multiple agvsreal factory environmenttask allocationfactory environmentsproposed algorithmsubsequently enhancedslightly highersignificantly lowerrunning timeoverall performanceload distanceskey topicintelligent manufacturingconducted basedbalance fairnessalso optimizing<div><p>Research on task allocation for multiple automated guided vehicles (AGVs) in factory environments is a key topic in intelligent manufacturing. Existing studies often struggle to balance fairness and priority in task allocation, leading to low AGV utilization and high no-load distances. Moreover, the stability and applicability of task allocation algorithms in real-world production environments face significant challenges. To address these issues, a mathematical model is formulated with the objective of minimizing the no-load distances of all AGVs in material delivery tasks. The model is subsequently enhanced by incorporating task allocation balance and priority. To solve the optimization model, an improved particle swarm optimization algorithm is proposed, and extensive simulation experiments are conducted based on a real factory environment. By comparing the optimization results of the proposed algorithm with those of the latest multi-population genetic algorithm (MGA) and the market-based bundle task allocation method (MBTA), it is evident that both the proposed algorithm and MGA achieve higher AGV utilization and shorter total task completion times than MBTA, while also optimizing no-load distances. Although the running time of the proposed algorithm is slightly higher than that of MBTA, it is significantly lower than that of MGA, and its overall performance in reducing no-load distances and enhancing AGV utilization is superior to that of MGA. The proposed method can be applied to guide multiple AGVs in multi-material delivery tasks in real factory environments.</p></div>2025-06-02T18:18:05ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0321616.g001https://figshare.com/articles/figure/Grid_map_model_of_the_factory_area_/29217526CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/292175262025-06-02T18:18:05Z |
| spellingShingle | Grid map model of the factory area. Yazhen Zhu (3595430) Ecology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> research material delivery tasks low agv utilization extensive simulation experiments enhancing agv utilization population genetic algorithm task allocation algorithms guide multiple agvs real factory environment task allocation factory environments proposed algorithm subsequently enhanced slightly higher significantly lower running time overall performance load distances key topic intelligent manufacturing conducted based balance fairness also optimizing |
| status_str | publishedVersion |
| title | Grid map model of the factory area. |
| title_full | Grid map model of the factory area. |
| title_fullStr | Grid map model of the factory area. |
| title_full_unstemmed | Grid map model of the factory area. |
| title_short | Grid map model of the factory area. |
| title_sort | Grid map model of the factory area. |
| topic | Ecology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> research material delivery tasks low agv utilization extensive simulation experiments enhancing agv utilization population genetic algorithm task allocation algorithms guide multiple agvs real factory environment task allocation factory environments proposed algorithm subsequently enhanced slightly higher significantly lower running time overall performance load distances key topic intelligent manufacturing conducted based balance fairness also optimizing |