基于Canopy和k-means算法的订单分批优化

Order batching optimization based on Canopy and k-means algorithm

文章针对物流企业的订单分批问题,提出了改进的Canopy-k-means算法。该算法是采用Canopy算法依据最大最小原则生成初始聚类中心,并使用k-means聚类算法对其进行优化获取分批结果的。此外,文章针对不同规模的订单数据集,比较了该算法和先来先服务(first come first served,FCFS)、k-means以及Canopy-k-means算法的实际效果,实验结果表明:该算法可以避免k-means算法中k值选取的盲目性,同时可以有效地提高分拣效率以及降低分拣批次。

To solve the order batching problem of logisitics company, the improved Canopy-k-means al- gorithm is proposed. In this algorithm, the initial cluster centers are generated based on the principle of maximum and minimum by using Canopy algorithm. Then the k-means clustering algorithm is used to optimize it and obtain batch results. For different sizes of order data sets, the comparison between the proposed algorithm and the first come first served（FCFS）, k-means and Canopy-k-means algo- rithm in terms of the actual effect is conducted. The experimental results show that the proposed method avoids the blindness in selecting k value in k-means algorithm, improves the sorting efficiency and reduces sorting batch numbers.