arXiv:2606.16330v1 Announce Type: new Abstract: Disruption recovery in industrial assembly lines requires timely decisions under machine faults, worker absence, and emergency orders. Existing methods either rely on rigid handcrafted recovery logic or learn adaptive policies that do not readily exploit heterogeneous external recovery knowledge at decision time to reduce abnormal recovery time (ART) and preserve on-time delivery (OTD). To address this gap, we propose a phase-aware guidance injection framework that augments a trained recurrent MAPPO (RMAPPO) scheduling policy through logit-level
The increasing complexity of industrial automation and the drive for greater efficiency and resilience in manufacturing push for more sophisticated AI-driven recovery solutions.
This development offers a method to enhance the robustness and responsiveness of industrial assembly lines, directly impacting manufacturing productivity and supply chain stability.
Existing recovery methods, often rigid or unable to integrate external knowledge, are augmented by adaptive policies that exploit diverse information sources for faster and more efficient disruption recovery.
- · Manufacturing companies
- · Automation solution providers
- · Industrial AI developers
- · Legacy industrial automation systems
- · Companies with high operational rigidities
Reduced downtime and increased throughput in industrial assembly lines.
Greater adoption of AI-driven tools in operational technology (OT) environments, potentially accelerating lights-out manufacturing ambitions.
Enhanced resilience of critical supply chains, reducing economic vulnerabilities to unforeseen disruptions.
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Read at arXiv cs.AI