Research on batching strategy of medical orders based on Canopy-K-means two-stage clustering algorithm

Abstract

With the sharp increase in the number of orders and the amount of dismantling and sorting in the pharmaceutical logistics center, how to save labor in a limited time and improve the efficiency of sorting orders for dismantling is a problem that needs to be solved urgently in the field of medicine circulation. This article focuses on the issue of order batching strategy based on the delivery to person picking system in the pharmaceutical industry to improve the efficiency of picking. Based on the characteristics of pharmaceutical orders and related policies, the number of identical items between the two orders is used as the order coupling factor to establish the order batching model. In order to solve the model, this paper proposes the Canopy-k-means two-stage clustering algorithm in which the Canopy algorithm is used to coarsely cluster the orders to reduce the dimensionality firstly, and then the K-means clustering algorithm is run to subdivide the batch. In addition, the MATLAB platform is used to simulate and verify the algorithm, the implementation effects of the algorithm and the first come first service (FCFS) algorithm and the K-means clustering algorithm are compared, and the effectiveness of the algorithm in terms of clustering effect and operating efficiency is verified.