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.

翻译：为设施布局问题（FLP）这一具有多目标权衡的NP难优化问题选择求解算法，是一项需要深厚专家知识的复杂任务。给定算法的性能取决于问题的具体特征，如设施数量、目标函数和约束条件。这催生了对数据驱动推荐方法的需求，以指导自动化设计系统中的算法选择。本文提出了一种新的推荐方法，基于知识图谱检索增强生成（KG-RAG）框架，使此类专业知识易于获取。在该框架中，首先从文献中构建领域特定的知识图谱（KG）。随后，该方法采用多维度检索机制，通过三种不同方式从该知识图谱中收集相关证据：精确的基于图谱的搜索、灵活的基于向量的搜索以及基于聚类的高层搜索。检索到的证据由大型语言模型（LLM）利用，基于数据驱动的推理生成算法推荐。该KG-RAG框架在一个包含六个复杂多目标、多约束FLP案例的应用场景中进行了测试，并将结果与Gemini 1.5 Flash聊天机器人进行了比较。结果显示，KG-RAG的平均推理得分达到4.7分（满分5分），而基线聊天机器人的平均得分为3.3分。