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Train small, deploy large: Scaling multi-agent reinforcement learning for multi-stage manufacturing lines.

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Title: Train small, deploy large: Scaling multi-agent reinforcement learning for multi-stage manufacturing lines.
Authors: Bhatta, Kshitij1 (AUTHOR) qpy8hh@virginia.edu, Chang, Qing1 (AUTHOR) qc9nq@virginia.edu
Source: Journal of Manufacturing Systems. Aug2025, Vol. 81, p155-168. 14p.
Subjects: Reinforcement learning, Manufacturing workstations, Multiagent systems, Manufacturing processes, Training of engineers
Abstract: We present a novel control framework using Multi Agent Reinforcement learning (MARL) that is scalable in the number of workstations in a multi-stage manufacturing line. We show that the dynamics of any production line, regardless of size, can be decoupled into three fundamental expressions. These expressions capture the dynamics of (1) the first workstation, (2) all intermediate workstations, and (3) the last workstation. This decoupling, combined with observation engineering enables training a characteristic 3-workstation, 2-buffer model using MARL methods, which can then generalize to production lines with w workstations with arbitrary cycle times, buffer capacities and reliability models. A numerical study is then conducted to validate the framework. • Decoupled line dynamics into 3 terms, valid for any number of workstations. • Framed control as DecPOMDP with scalable observation representation. • Showed how optimality transfers from 3-workstation to w -workstation policies. • Demonstrated how 3-workstation model generalizes to w workstations with varied settings. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Manufacturing Systems is the property of Elsevier B.V. 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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PubType: Academic Journal
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  Data: <searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+workstations%22">Manufacturing workstations</searchLink><br /><searchLink fieldCode="DE" term="%22Multiagent+systems%22">Multiagent systems</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+processes%22">Manufacturing processes</searchLink><br /><searchLink fieldCode="DE" term="%22Training+of+engineers%22">Training of engineers</searchLink>
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  Data: We present a novel control framework using Multi Agent Reinforcement learning (MARL) that is scalable in the number of workstations in a multi-stage manufacturing line. We show that the dynamics of any production line, regardless of size, can be decoupled into three fundamental expressions. These expressions capture the dynamics of (1) the first workstation, (2) all intermediate workstations, and (3) the last workstation. This decoupling, combined with observation engineering enables training a characteristic 3-workstation, 2-buffer model using MARL methods, which can then generalize to production lines with w workstations with arbitrary cycle times, buffer capacities and reliability models. A numerical study is then conducted to validate the framework. • Decoupled line dynamics into 3 terms, valid for any number of workstations. • Framed control as DecPOMDP with scalable observation representation. • Showed how optimality transfers from 3-workstation to w -workstation policies. • Demonstrated how 3-workstation model generalizes to w workstations with varied settings. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Manufacturing Systems is the property of Elsevier B.V. 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:
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      – Type: doi
        Value: 10.1016/j.jmsy.2025.04.017
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      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 155
    Subjects:
      – SubjectFull: Reinforcement learning
        Type: general
      – SubjectFull: Manufacturing workstations
        Type: general
      – SubjectFull: Multiagent systems
        Type: general
      – SubjectFull: Manufacturing processes
        Type: general
      – SubjectFull: Training of engineers
        Type: general
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      – TitleFull: Train small, deploy large: Scaling multi-agent reinforcement learning for multi-stage manufacturing lines.
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            NameFull: Bhatta, Kshitij
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            NameFull: Chang, Qing
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            – D: 01
              M: 08
              Text: Aug2025
              Type: published
              Y: 2025
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              Value: 81
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