ReflecSched: Solving Dynamic Flexible Job-Shop Scheduling via LLM-Powered Hierarchical Reflection

TL;DR

ReflecSched enhances LLM-based dynamic job-shop scheduling by integrating strategic analysis of heuristic simulations, resulting in superior, efficient, and non-myopic decision-making in complex manufacturing environments.

Contribution

The paper introduces ReflecSched, a novel framework that equips LLMs with strategic reflection capabilities, overcoming limitations of direct application in dynamic flexible job-shop scheduling.

Findings

Achieves an average RPD of 6.09% on GEN-Bench.

Outperforms traditional and learning-based methods like HMPSAC and IDDQN.

Secures a 71.35% Win Rate over baseline methods.

Abstract

The NP-hard Dynamic Flexible Job-Shop Scheduling (DFJSP) problem involves real-time events and complex routing. While traditional rules are efficient but rigid, deep learning is opaque and requires feature engineering. Large Language Models (LLMs) promise adaptive reasoning without this engineering overhead, yet we find their direct application is suboptimal. Baseline LLMs suffer from three key pitfalls: the long-context paradox, where crucial data is underutilized; an underutilization of expert heuristics; and myopic decision-making. To address this, we propose ReflecSched, a framework that empowers the LLM beyond a direct scheduler by equipping it with a strategic analysis capability. ReflecSched tasks the LLM to analyze heuristic-driven simulations across multiple planning horizons and distill them into a concise, natural-language summary termed Strategic Experience. This summary is then integrated into the prompt of a final decision-making module, guiding it to produce non-myopic actions. Experiments demonstrate ReflecSched achieves superior performance, with its best variants attaining an average RPD of 6.09% and rank of 4.39 on GEN-Bench, significantly outperforming strong traditional and learning-based methods including HMPSAC and IDDQN. It also statistically and decisively surpasses direct LLM baselines, securing a 71.35% Win Rate while being, on average, 15.1% more token-efficient on Normal-scale problems. Furthermore, cumulative runtime analysis reveals that ReflecSched's zero-shot nature eliminates the training bottleneck, providing a decisive efficiency advantage in high-variability manufacturing environments. Ablation studies attribute this performance to a robust reflection mechanism that leverages high-quality, contrastive experience. Ultimately, the framework's performance is statistically on par with an oracle-like strategy, showcasing its effectiveness and robustness.