基于聚类的仓储物料周转时间优化

Yong Li, Xiaoyun Tian, Jiangkai Jia, Bin Zheng, Hairu Li, Mingda Wang, Ximin Sun
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引用次数: 0

摘要

仓储物流业是支撑国民经济发展的基础性、战略性、主导性产业。必须努力提高周转时间内仓储物流的智能化水平。电力领域的仓储物流规模大、范围广。在本文中,我们使用指数平滑算法来压缩大量数据,同时消除极端数据。采用K-means和DBSCAN算法处理与仓储物料周转时间相关的数据因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization of Warehousing Material Turnover Time Based on Clustering
The warehousing and logistics industry is a basic, strategic and leading industry that supports the development of the national economy. Efforts must be made to improve the intelligent level of warehousing and logistics in the Turnover Time. Warehousing and logistics in the power field are large in scale and wide in scope. In this paper, we use exponential smoothing algorithm to compress large amounts of data while eliminating extreme data. K-means and DBSCAN algorithms are used to deal with data factors related to the turnover time of warehousing materials.
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