Flexible material handling system for multi-load autonomous mobile robots in manufacturing environments: a hierarchical reinforcement learning approach.
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| Title: | Flexible material handling system for multi-load autonomous mobile robots in manufacturing environments: a hierarchical reinforcement learning approach. |
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| Authors: | Park, Keonwoo1 (AUTHOR), Jo, Seongbae1 (AUTHOR), Shin, Youngchul2 (AUTHOR), Moon, Ilkyeong1,3 (AUTHOR) ikmoon@snu.ac.kr |
| Source: | International Journal of Production Research. Aug2025, Vol. 63 Issue 15, p5671-5691. 21p. |
| Subjects: | Automated materials handling, Reinforcement learning, Production scheduling, Mobile robots, Graph neural networks, Robotic path planning, Manufacturing industries, Process optimization |
| Abstract: | The increasing complexity of customer demands has led to the implementation of flexible job-shop scheduling and automated material handling systems across manufacturing sectors. In particular, advances in robotic technology have made autonomous mobile robots (AMRs) essential for material handling tasks within these sectors. The capability of free movement, path planning, and loading multiple work-in-processes (WIPs) can significantly enhance the efficiency of material handling operations. However, the full flexibility of AMRs cannot be utilised when their decisions regarding the sequence of loading and unloading multiple WIPs are made by specific rule-based operations, resulting in inefficiencies in the throughput of WIPs in manufacturing environments. To address this inefficiency, we introduce a hierarchical reinforcement learning algorithm to optimise material handling with AMRs, thereby maximising the throughput of WIPs. In this approach, a graph attention network (GAT) serves as an encoder for the hierarchical reinforcement learning (HRL) input, effectively capturing the complex relationships between different nodes. Computational experiments demonstrate that our approach enhances the efficiency of the material handling system more effectively than existing rule-based methods. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 186989305 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Flexible material handling system for multi-load autonomous mobile robots in manufacturing environments: a hierarchical reinforcement learning approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Park%2C+Keonwoo%22">Park, Keonwoo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jo%2C+Seongbae%22">Jo, Seongbae</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shin%2C+Youngchul%22">Shin, Youngchul</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Moon%2C+Ilkyeong%22">Moon, Ilkyeong</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> ikmoon@snu.ac.kr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Aug2025, Vol. 63 Issue 15, p5671-5691. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Automated+materials+handling%22">Automated materials handling</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Production+scheduling%22">Production scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+robots%22">Mobile robots</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Robotic+path+planning%22">Robotic path planning</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+industries%22">Manufacturing industries</searchLink><br /><searchLink fieldCode="DE" term="%22Process+optimization%22">Process optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The increasing complexity of customer demands has led to the implementation of flexible job-shop scheduling and automated material handling systems across manufacturing sectors. In particular, advances in robotic technology have made autonomous mobile robots (AMRs) essential for material handling tasks within these sectors. The capability of free movement, path planning, and loading multiple work-in-processes (WIPs) can significantly enhance the efficiency of material handling operations. However, the full flexibility of AMRs cannot be utilised when their decisions regarding the sequence of loading and unloading multiple WIPs are made by specific rule-based operations, resulting in inefficiencies in the throughput of WIPs in manufacturing environments. To address this inefficiency, we introduce a hierarchical reinforcement learning algorithm to optimise material handling with AMRs, thereby maximising the throughput of WIPs. In this approach, a graph attention network (GAT) serves as an encoder for the hierarchical reinforcement learning (HRL) input, effectively capturing the complex relationships between different nodes. Computational experiments demonstrate that our approach enhances the efficiency of the material handling system more effectively than existing rule-based methods. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00207543.2025.2461131 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 5671 Subjects: – SubjectFull: Automated materials handling Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Production scheduling Type: general – SubjectFull: Mobile robots Type: general – SubjectFull: Graph neural networks Type: general – SubjectFull: Robotic path planning Type: general – SubjectFull: Manufacturing industries Type: general – SubjectFull: Process optimization Type: general Titles: – TitleFull: Flexible material handling system for multi-load autonomous mobile robots in manufacturing environments: a hierarchical reinforcement learning approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Park, Keonwoo – PersonEntity: Name: NameFull: Jo, Seongbae – PersonEntity: Name: NameFull: Shin, Youngchul – PersonEntity: Name: NameFull: Moon, Ilkyeong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 63 – Type: issue Value: 15 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
