Order batching method based on improved K-Means algorithm

Abstract

The invention discloses an order batching method based on an improved K-Means algorithm, and the method is based on data mining. The method comprises the following steps: 1, conducting vectorization of a data set and obtaining an order set X; 2, obtaining a distance threshold T1 and a distance threshold T2 through a cross-validation method; 3, using a Canopy algorithm to obtain a cluster number K and a center point; 4, using the K and the center point obtained in the previous step and the improved K-Means algorithm to conduct clustering; and 5, after obtaining a final clustering result, sorting orders according to the average arrival time of the orders of each cluster, and obtaining a result of order batching. The method can accurately batch a large number of logistics orders, so that the efficiency of sorting operation is improved and the time taken for the sorting step is reduced.

Description

technical field [0001] The invention belongs to the field of data mining, in particular to an order batching method based on an improved K-Means algorithm. Background technique [0002] With the advent of the online shopping era, e-commerce companies will generate a large number of logistics orders. The number of these orders is massive, and the items in the orders are characterized by small batches, multiple varieties, and multiple batches. It's very difficult. The sorting link is the most time-consuming link in the entire order fulfillment link except transportation. The average sorting time accounts for more than 40% of the order fulfillment time in the warehouse. Sorting according to the order of order arrival time is not only time-consuming and laborious, but also easy Mistakes, inefficiencies. [0003] Order batching is an NP-hard problem, which cannot be effectively solved using traditional precise algorithms, so heuristic algorithms are generally used to solve it. ...

AI Technical Summary

Problems solved by technology

[0004] Among these algorithms, the priority rule algorithm is relatively simple, but it does not consider the correlation between orders, resulting in the repetition of sorting paths; the selection of the initial value of the seed algorithm is difficult to determine; and the saving algorithm only considers the target sorting distance The shortest, without in-depth consideration of the attributes of the order itself and the relationship between orders

The traditional heuristic algorithm can only be calculated for small data sets, while the K-Means algorithm can handle large data sets. The algorithm is scalable and efficient, and can efficiently process the data of the current massive orders. However, the currently used The literature on the K-Means clustering method only considers attributes such as transportation and processing time, does not consider the number of repeated channels in the order item, and lacks a reasonable selection of the initial clustering center, and is prone to fall into local optimum

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