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Shopfloor layout generation method based on large language models
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AI
DOI：10.1080/0951192X.2025.2482258.png)
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Abstract
En 中文
Traditional shopfloor layout design faces limitations such as high complexity in model construction and excessive reliance on individual designers' experience, which cannot meet the diverse needs of modern industrial production. To obtain solutions with adaptive and customizable potential, this paper introduces a current cutting-edge technology - Large Language Models (LLMs). The intention is to leverage their spatial reasoning and generative capabilities to open up new avenues for solving such problems. This paper proposes a shopfloor layout generation method based on Prompt Engineering. By structurally defining and semantically transforming the layout space and task requirements, integrating the multi-dimensional information of the shopfloor as the knowledge support of the generation process, and constructing an iterative cycle of 'think-generate-evaluate', a stable generation of effective shopfloor layouts is achieved. This paper constructed a specialized Q&A dataset to evaluate the boundaries and potential of large language models in the field of shopfloor layout. Additionally, an experimental validation was conducted on three representative layout patterns in typical discrete manufacturing shopfloor scenarios, strongly demonstrating the innovative value and practical significance of the method.
Keywords:
Large language models
shopfloor layout
prompt engineering
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