用知识图谱增强大模型，智能推荐工厂布局问题的算法方案。
A Knowledge Graph-based Retrieval-Augmented Generation Framework for Algorithm Selection in the Facility Layout Problem
- 构建领域知识图谱，融合三种检索方式获取问题特征
- 在6个复杂案例中平均推理得分4.7（满分5），优于基线3.3
- 适合自动化设计系统中的算法选型，尤其适合多目标优化场景
针对设施布局问题（FLP）这一具有多目标权衡的NP难优化问题，算法选择依赖专家经验，其性能受设施数量、目标与约束特性影响。本文提出基于知识图谱的检索增强生成框架（KG-RAG），从文献中构建领域知识图谱，通过图结构搜索、向量搜索与聚类搜索三种方式检索相关证据，由大语言模型结合数据驱动推理生成算法推荐。在包含6个复杂多目标、多约束FLP案例的应用中，该框架平均推理得分达4.7（满分5），显著优于基线模型Gemini 1.5 Flash的3.3分。
原文摘要 · Abstract (English)
Selecting a solution algorithm for the Facility Layout Problem (FLP), an NP-hard optimization problem with multiobjective trade-off, is a complex task that requires deep expert knowledge. The performance of a given algorithm depends on the specific characteristics of the problem, such as the number of facilities, objectives, and constraints. This creates a need for a data-driven recommendation method to guide algorithm selection in automated design systems. This paper introduces a new recommendation method to make this expertise accessible, based on a Knowledge Graph-Based Retrieval-Augmented Generation (KG-RAG) framework. In this framework, a domain-specific knowledge graph (KG) is constructed from the literature. The method then employs a multifaceted retrieval mechanism to gather relevant evidence from this KG using three distinct approaches: precise graph-based search, flexible vector-based search, and cluster-based high-level search. The retrieved evidence is utilized by a Large Language Model (LLM) to generate algorithm recommendations based on data-driven reasoning. This KG-RAG framework is tested on a use case consisting of six problems comprising of complex multi-objective and multi-constraint FLP case. The results are compared with the Gemini 1.5 Flash chatbot. The results show that KG-RAG achieves an average reasoning score of 4.7 out of 5 compared to 3.3 for the baseline chatbot.
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