基于聚类分析与需求预测的电力企业仓储网络优化与研究

Optimization and Research of Power Enterprise Warehouse Network Based on Cluster Analysis and Demand Forecasting

摘要: 本文旨在解决电力企业仓网布局不合理、库存成本高等问题，并提出一种基于聚类分析与需求预测的优化方法。该方法利用K-means算法对仓库进行空间聚类以生成初步方案，继而通过标准托盘位当量测算库容，并建立时间序列预测模型来验证方案的可行性。最后，运用层次分析法进行多维度评价分析，形成闭环优化流程。本研究有助于优化电力企业仓网布局，为降低运营成本、提升服务效率及物资管理科学决策提供支持。

Abstract: This paper aims to address issues such as unreasonable warehouse network layouts and high inventory costs in power enterprises, proposing an optimization method based on cluster analysis and demand forecasting. The method employs the K-means algorithm to perform spatial clustering of warehouses, generating a preliminary plan. Subsequently, it calculates storage capacity using Standard Pallet Position Equivalent (SPPE) and establishes a time series forecasting model to validate the plan’s feasibility. Finally, a multi-dimensional evaluation analysis is conducted using the Analytic Hierarchy Process (AHP), forming a closed-loop optimization process. This research contributes to optimizing warehouse network layouts for power enterprises, supporting reduced operational costs, enhanced service efficiency, and scientific decision-making in material management.