RACE-Sched is an asynchronous dual-stream agent framework combining low-latency symbolic heuristics with parallel LLM-based rule synthesis and sandbox validation for dynamic flexible job shop scheduling.
Reflecsched: Solving dynamic flexible job-shop scheduling via llm-powered hierarchical reflection,
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
MAFIG uses a Perception Agent and Emergency Decision Agent plus span-focused local distillation to let lightweight models rapidly generate formal instructions that fix local scheduling failures, achieving over 94% success with sub-second latency on port, warehousing, and deck datasets.
A two-stage reflection and reprompting framework is proposed to improve LLM reliability when generating feasible sequences for Petri net reachability problems in manufacturing.
citing papers explorer
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Harmonizing Real-Time Constraints and Long-Horizon Reasoning: An Asynchronous Agentic Framework for Dynamic Scheduling
RACE-Sched is an asynchronous dual-stream agent framework combining low-latency symbolic heuristics with parallel LLM-based rule synthesis and sandbox validation for dynamic flexible job shop scheduling.
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MAFIG: Multi-agent Driven Formal Instruction Generation Framework
MAFIG uses a Perception Agent and Emergency Decision Agent plus span-focused local distillation to let lightweight models rapidly generate formal instructions that fix local scheduling failures, achieving over 94% success with sub-second latency on port, warehousing, and deck datasets.
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A Two-Stage Reflection and Reprompting Framework for LLM-Based Solution of Petri Net Reachability Problems in Industrial Applications
A two-stage reflection and reprompting framework is proposed to improve LLM reliability when generating feasible sequences for Petri net reachability problems in manufacturing.