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LLMs can Schedule
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The job shop scheduling problem (JSSP) remains a significant hurdle in optimizing production processes. This challenge involves efficiently allocating jobs to a limited number of machines while minimizing factors like total processing time or job delays. While recent advancements in artificial intelligence have yielded promising solutions, such as reinforcement learning and graph neural networks, this paper explores the potential of Large Language Models (LLMs) for JSSP. We introduce the very first supervised 120k dataset specifically designed to train LLMs for JSSP. Surprisingly, our findings demonstrate that LLM-based scheduling can achieve performance comparable to other neural approaches. Furthermore, we propose a sampling method that enhances the effectiveness of LLMs in tackling JSSP.
Forward citations
Cited by 3 Pith papers
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Guiding Large Language Models with Genetic Programming-Evolved Heuristic Knowledge for Dynamic Multi-Mode Project Scheduling
GP-evolved priority rules, injected as feature restrictions or explicit rules, improve LLM project-scheduling decisions, token efficiency, and decision stability compared with unguided prompting.
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PowerAtlas: Towards Electricity-Computing Co-Scheduling for Power Systems
SFT plus feasibility-aware GRPO lets 3B LLMs produce electricity–computing co-schedules that are far more grid-feasible and cheaper than untrained or frontier zero-shot baselines on ECBench.
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A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving
A literature survey that classifies LLM-based optimization research into modeling and solving, with solving divided into LLMs as optimizers, low-level components, and high-level managers.
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