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This paper introduces RACE-Sched, an asynchronous agentic framework that decouples real-time reactive scheduling from deliberative LLM-based reasoning to handle dynamic job shop scheduling problems, achieving superior performance over DRL and other baselines.
This paper proposes a policy-neutral execution and measurement layer to bridge the sim-to-real gap in reinforcement learning-based industrial dispatching, enabling structured attribution of execution errors and improving reliability and interpretability.
This paper proposes a gated hyper-heuristic for job shop scheduling that uses regret-normalized rollout labels and contextual KNN uncertainty estimates to reduce label generation costs and avoid switching away from strong default rules unless the predicted improvement is credible. Experiments show the gated selector achieves low mean relative percentage deviation while significantly reducing computational cost.