MAFIG: Multi-agent Driven Formal Instruction Generation Framework
Summary: arXiv:2604.10989v1 Announce Type: new
Abstract: Emergency situations in scheduling systems often trigger local functional failures that undermine system stability and even cause system collapse. Existing methods primarily rely on robust scheduling or reactive scheduling, handling emergencies through predefined rules or rescheduling strategies. However, the diversity and unpredictability of real-world emergencies make them difficult to anticipate, which limits the adaptability of these methods in complex scenarios.
Recent studies have shown that Large Language Models (LLMs) possess strong potential for complex scheduling tasks because of their extensive prior knowledge and strong reasoning capabilities. Nevertheless, the high inference latency of LLMs and the lengthy contextual information of scheduling systems significantly hinder their application for emergency handling.
Introducing MAFIG
To mitigate these issues, we propose the Multi-agent Driven Formal Instruction Generation Framework (MAFIG). This innovative framework constrains the decision scope to local functional modules affected by emergency situations and repairs scheduling logic rapidly by generating formal instructions.
Key Components of MAFIG
- Perception Agent: This component is responsible for assessing the current state of the scheduling system and identifying potential emergencies.
- Emergency Decision Agent: This agent facilitates quick decision-making by processing information from the Perception Agent and generating formal instructions to mitigate the impact of emergencies.
Addressing Latency Challenges
We further introduce a span-focused loss-driven local distillation mechanism (SFL) to enhance the performance of the framework. The SFL mechanism transfers the decision-making capability of powerful Cloud Large Language Models (C-LLMs) to lightweight local models. This transfer reduces inference latency while preserving decision-making effectiveness, enabling faster responses to emergencies.
Experimental Results
Experiments conducted using Port, Warehousing, and Deck scheduling datasets demonstrated the effectiveness of the MAFIG framework. The results indicate:
- Success rate in Port scheduling: 98.49%
- Success rate in Warehousing scheduling: 94.97%
- Success rate in Deck scheduling: 97.50%
Additionally, the average processing times for these datasets were:
- Port: 0.33 seconds
- Warehousing: 0.23 seconds
- Deck: 0.19 seconds
Conclusion
The results demonstrate that MAFIG effectively mitigates the impact of emergencies on scheduling systems and significantly improves their robustness and adaptability. This framework represents a promising advancement in the field of emergency management within complex scheduling environments, paving the way for more resilient systems in the future.