用混合神经网络优化多级供应链，降低需求波动影响
Optimizing Multi-Tier Supply Chain Ordering with a Hybrid Liquid Neural Network and Extreme Gradient Boosting Model
- 融合液态神经网络与梯度提升树，动态提取特征并全局优化
- 在自动售货机测试中显著降低牛鞭效应，提升利润空间
- 适合需要实时响应的智能供应链系统开发者
供应链管理（SCM）面临需求波动和牛鞭效应等重大挑战。传统方法及前沿大模型在自动售货机测试等基准上表现不佳，难以处理供应链复杂的连续时间序列数据。尽管LSTM和XGBoost等机器学习方法提供解决方案，但常受限于计算效率。液态神经网络（LNN）在机器人领域表现出强适应性与高效率，但在SCM中尚未被应用。本文提出一种LNN+XGBoost混合模型，用于多级供应链订货优化。通过结合LNN的动态特征提取能力与XGBoost的全局优化优势，旨在最小化牛鞭效应并提升盈利能力。该创新方法填补了智能供应链管理中对高效性与自适应性的关键需求空白。
原文摘要 · Abstract (English)
Supply chain management (SCM) faces significant challenges like demand fluctuations and the bullwhip effect. Traditional methods and even state-of-the-art LLMs struggle with benchmarks like the Vending Machine Test, failing to handle SCM's complex continuous time-series data. While ML approaches like LSTM and XGBoost offer solutions, they are often limited by computational inefficiency. Liquid Neural Networks (LNN), known for their adaptability and efficiency in robotics, remain untapped in SCM. This study proposes a hybrid LNN+XGBoost model for multi-tier supply chains. By combining LNN's dynamic feature extraction with XGBoost's global optimization, the model aims to minimize the bullwhip effect and increase profitability. This innovative approach addresses the need for efficiency and adaptability, filling a critical gap in intelligent SCM.
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。