How can LLM agents work together to schedule factory production dynamically?
Scalable Multi-agent Reinforcement Learning for Factory-wide Dynamic Scheduling
September 23, 2024
https://arxiv.org/pdf/2409.13571This paper presents a novel Multi-agent Reinforcement Learning (MARL) system for solving factory-wide dynamic scheduling problems. It tackles the issue of optimizing production schedules in complex manufacturing environments with fluctuating demand, machine maintenance, and operational constraints.
Here are the key points relevant to LLM-based multi-agent systems:
- Leader-follower architecture: The system uses a hierarchical multi-agent approach, where a "leader" agent coordinates multiple "follower" agents, each responsible for a specific operation. This allows for scalability and efficient decision-making in large-scale environments.
- Abstract goals: The leader communicates with followers through abstract goal vectors, providing high-level guidance without dictating specific actions. This allows followers to adapt to local conditions while still working towards overall system optimization.
- Rule-based conversion algorithm: To prevent catastrophic failures from suboptimal agent decisions, a rule-based system is implemented that can override agent actions regarding product conversions on machines, ensuring operational constraints are met.
This approach moves away from traditional methods relying solely on predefined rules or heuristics, allowing for more robust and flexible scheduling in dynamic environments. The use of a leader-follower structure and abstract goals for communication presents valuable insights for developing LLM-based multi-agent systems.