Task sequences for each AGV when using greedy algorithm in the front end of IPSO(+B).
<p>Task sequences for each AGV when using greedy algorithm in the front end of IPSO(+B).</p>
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1852019750747504640 |
|---|---|
| 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 | 2025-06-02T18:18:18Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0321616.t007 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Task_sequences_for_each_AGV_when_using_greedy_algorithm_in_the_front_end_of_IPSO_B_/29217565 |
| 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 | Task sequences for each AGV when using greedy algorithm in the front end of IPSO(+B). |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Task sequences for each AGV when using greedy algorithm in the front end of IPSO(+B).</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_ea2d9e3370699a057f2bc0dc8bd3c607 |
| identifier_str_mv | 10.1371/journal.pone.0321616.t007 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29217565 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Task sequences for each AGV when using greedy algorithm in the front end of IPSO(+B).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<p>Task sequences for each AGV when using greedy algorithm in the front end of IPSO(+B).</p>2025-06-02T18:18:18ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0321616.t007https://figshare.com/articles/dataset/Task_sequences_for_each_AGV_when_using_greedy_algorithm_in_the_front_end_of_IPSO_B_/29217565CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/292175652025-06-02T18:18:18Z |
| spellingShingle | Task sequences for each AGV when using greedy algorithm in the front end of IPSO(+B). 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 | Task sequences for each AGV when using greedy algorithm in the front end of IPSO(+B). |
| title_full | Task sequences for each AGV when using greedy algorithm in the front end of IPSO(+B). |
| title_fullStr | Task sequences for each AGV when using greedy algorithm in the front end of IPSO(+B). |
| title_full_unstemmed | Task sequences for each AGV when using greedy algorithm in the front end of IPSO(+B). |
| title_short | Task sequences for each AGV when using greedy algorithm in the front end of IPSO(+B). |
| title_sort | Task sequences for each AGV when using greedy algorithm in the front end of IPSO(+B). |
| 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 |