用大模型优化2D装箱问题，效率更高且更省资源。
Evaluating LLMs for Combinatorial Optimization: One-Phase and Two-Phase Heuristics for 2D Bin-Packing
- 结合大模型与进化算法，迭代生成优化解
- GPT-4o两轮内达最优，平均用箱数从16减至15
- 适合研究大模型在组合优化中的应用者
本文提出一个评估大语言模型（LLMs）在组合优化中能力的框架，聚焦于二维装箱问题。通过将大模型与进化算法结合，系统性地生成并迭代优化启发式解。在对比传统方法（有限首次适应和混合首次适应）的全面实验中，结果表明大模型能生成更高效的解，同时计算资源消耗更低。评估显示，GPT-4o仅需两轮迭代即可达到最优解，平均箱数由16降至15，空间利用率从0.76–0.78提升至0.83。本工作为理解大模型在特定领域的评估提供了依据，并建立了组合优化任务中的性能基准。
原文摘要 · Abstract (English)
This paper presents an evaluation framework for assessing Large Language Models' (LLMs) capabilities in combinatorial optimization, specifically addressing the 2D bin-packing problem. We introduce a systematic methodology that combines LLMs with evolutionary algorithms to generate and refine heuristic solutions iteratively. Through comprehensive experiments comparing LLM generated heuristics against traditional approaches (Finite First-Fit and Hybrid First-Fit), we demonstrate that LLMs can produce more efficient solutions while requiring fewer computational resources. Our evaluation reveals that GPT-4o achieves optimal solutions within two iterations, reducing average bin usage from 16 to 15 bins while improving space utilization from 0.76-0.78 to 0.83. This work contributes to understanding LLM evaluation in specialized domains and establishes benchmarks for assessing LLM performance in combinatorial optimization tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。