Harmonizing Real-Time Constraints and Long-Horizon Reasoning: An Asynchronous Agentic Framework for Dynamic Scheduling
arXiv:2605.29262v1 Announce Type: new Abstract: The Dynamic Flexible Job Shop Scheduling Problem (DFJSP) necessitates a trade-off between instant reaction to stochastic disturbances and global optimization of production goals. Conventional priority rules are insufficiently flexible to handle complex disruptions, whereas learning-based approaches often compromise interpretability or fail to generalize across problem scales. Although Large Language Models (LLMs) offer advanced reasoning capabilities to bridge this gap, their substantial inference latency is incompatible with the millisecond-leve
LLMs are advancing rapidly, but their inherent latency has been a critical barrier to real-time applications such as dynamic scheduling problems, creating a pressing need for architectural solutions.
This research addresses a core challenge of integrating advanced AI reasoning into operational systems, enabling LLMs to move beyond purely analytical roles into real-time decision-making for complex industrial processes.
The development of asynchronous agentic frameworks allows LLMs to participate in time-sensitive operations without direct real-time inference, unlocking new applications in automation and control.
- · Logistics and Supply Chain Management
- · Manufacturing Automation
- · AI Agents Developers
- · Edge AI Providers
- · Traditional Scheduling Software
- · Manual Process Management
- · Legacy AI Optimization Systems
Companies can implement more adaptable and efficient scheduling systems, reducing operational costs and improving response to disruptions.
This framework could accelerate the deployment of agentic AI in other real-time operational domains, from robotics to intelligent infrastructure.
The enhanced integration of AI into physical systems could lead to more resilient and autonomous industrial ecosystems, shifting labor requirements and skill sets.
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Read at arXiv cs.AI