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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]
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Database: Engineering Source
Description
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]
ISSN:02786125
DOI:10.1016/j.jmsy.2025.04.017