An Order Grouping Method Based on Improved Binary K-Means Algorithm

Abstract

The invention discloses an order grouping method based on the improved binary k-means algorithm. The improved binary k-means algorithm is used to solve the order batching problem of the distribution center, and three aspects are selected from the selection of k value, the selection of the initial central value, and the processing of abnormal points. In one aspect, the k-means clustering algorithm has been improved to avoid the algorithm from falling into local optimum, making the solution of the order grouping scheme more effective and reasonable, thereby effectively improving the efficiency of order sorting.

Description

technical field [0001] The invention relates to a grouping method, in particular to an order grouping method based on an improved binary k-means algorithm. Background technique [0002] With the development of e-commerce, e-commerce will receive a large number of orders every day, and these orders are characterized by small batches, multiple varieties, and multiple batches. For these large-scale orders, the pressure on e-commerce logistics centers is increasing. [0003] Order grouping is to group the collected customer orders according to specific rules, and arrange the same group of orders on the same workbench for sorting, so as to shorten the order picking time and improve picking efficiency. Currently, order grouping strategies include: 1. Seed algorithm, 2. Saving algorithm, 3. Priority rule algorithm, 4. Heuristic algorithm, 5. Data mining algorithm. [0004] It is difficult to find a better value for the initial seed data of the seed algorithm, and it is difficult ...

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Problems solved by technology

[0004] It is difficult to find a better value for the initial seed data of the seed algorithm, and it is difficult to find the optimal grouping scheme for large-scale orders; the optimal rule algorithm is to classify customer orders and group them according to the priority of the order, but it does not consider the order The correlation between the obtained grouping schemes often cannot effectively reduce the sorting efficiency; and the general heuristic algorithm cannot be applied to large-scale order collections; for data mining algorithms, there are mainly two types of order grouping problems at this stage: Algorithm: association rule mining, k-means clustering algorithm

These two algorithms are suitable for large-scale order grouping problems, but the current order grouping scheme based on the k-means algorithm does not have three limitations of the reasonable improvement algorithm: 1. Determination of the k value, 2. Determination of the initial center, 3. The processing of abnormal data points makes the current scheme fail to effectively improve the sorting efficiency of the system

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