Constrained K-Means Algorithm and Its Application in Distribution Center Location Problem

Abstract

To solve the constrained clustering problem, this paper improves the K-means and proposes a constrained K-means algorithm (CK-means). CK-means algorithm takes into account both clustering analysis and constraints, and can effectively deal with clustering problems with constraints, such as distribution center location problem with warehouse capacity constraints, vehicle routing problem with capacity constraints, etc. It has higher practical value and a wider range of applications. There are two core innovations of the CK-means algorithm: firstly, incorporating constraints into the K-means. The second is a search strategy based on sample weights. In addition, this paper also applies the CK-means algorithm to the location problem of distribution stations at the end of JD Logistics’ supply chain. The experimental results show that the CK-means can solve the clustering problem with constraints with effect.