<ul><li>Large Language Models (LLMs) have shown remarkable capabilities across various domains, but their potential for solving combinatorial optimization problems remains largely unexplored.</li><li>Researchers have introduced Starjob, the first supervised dataset for the Job Shop Scheduling Problem (JSSP), consisting of 130k instances designed for training LLMs.</li><li>By leveraging the Starjob dataset, researchers fine-tuned the LLaMA 8B 4-bit quantized model with the LoRA method to develop an end-to-end scheduling approach.</li><li>Evaluation on standard benchmarks showed that the LLM-based method outperformed traditional Priority Dispatching Rules (PDRs) and achieved notable improvements over state-of-the-art neural approaches, highlighting the potential of LLMs in tackling combinatorial optimization problems.</li></ul>

Starjob: Dataset for LLM-Driven Job Shop Scheduling

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