用知识图谱和大模型分析仓储仿真数据，自动找瓶颈
Leveraging Knowledge Graphs and LLM Reasoning to Identify Operational Bottlenecks for Warehouse Planning Assistance
- 将仿真数据转为语义丰富的知识图谱，支持结构化查询
- 大模型迭代提问并自检，对复杂问题诊断准确率显著提升
- 适合需要快速定位仓储运营问题的工程师与管理者
分析仓库运营的离散事件仿真（DES）输出数据以识别瓶颈和低效环节，是关键但极具挑战的任务，常需大量人工或专用工具。本框架结合知识图谱（KG）与基于大语言模型（LLM）的智能体，处理复杂的DES输出数据。它将原始数据转化为富含语义的KG，捕捉仿真事件与实体间的关系。LLM智能体通过迭代推理生成相互依赖的子问题，为每个问题构建Cypher查询，交互式获取信息并自我反思纠错。该自适应、迭代且自修正过程可模拟人类分析，有效识别运营问题。在设备故障与流程异常测试中，该方法优于基线，对操作性问题实现接近完美的定位准确率；对复杂诊断问题，展现出发现细微、关联性问题的卓越能力。本研究融合仿真建模与AI（KG+LLM），提供更直观的可行动洞察，缩短分析时间，实现仓储效率问题的自动化评估与诊断。
原文摘要 · Abstract (English)
Analyzing large, complex output datasets from Discrete Event Simulations (DES) of warehouse operations to identify bottlenecks and inefficiencies is a critical yet challenging task, often demanding significant manual effort or specialized analytical tools. Our framework integrates Knowledge Graphs (KGs) and Large Language Model (LLM)-based agents to analyze complex Discrete Event Simulation (DES) output data from warehouse operations. It transforms raw DES data into a semantically rich KG, capturing relationships between simulation events and entities. An LLM-based agent uses iterative reasoning, generating interdependent sub-questions. For each sub-question, it creates Cypher queries for KG interaction, extracts information, and self-reflects to correct errors. This adaptive, iterative, and self-correcting process identifies operational issues mimicking human analysis. Our DES approach for warehouse bottleneck identification, tested with equipment breakdowns and process irregularities, outperforms baseline methods. For operational questions, it achieves near-perfect pass rates in pinpointing inefficiencies. For complex investigative questions, we demonstrate its superior diagnostic ability to uncover subtle, interconnected issues. This work bridges simulation modeling and AI (KG+LLM), offering a more intuitive method for actionable insights, reducing time-to-insight, and enabling automated warehouse inefficiency evaluation and diagnosis.
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