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In this paper, we focus on the problem of the siting of distribution stations, which is of key importance during designing a logistics distribution network. This problem includes dealing with physical obstacles in real world, making the orders as close as possible to their respective stations and making the workload of each station as balanced as possible. To solve this problem, we use a dataset of the geographical coordinates of all orders of a certain express company per day in Shanghai, and propose an algorithm called Balanced COD-CLARANS, which is a constrained clustering algorithm capable of handling physical obstacles and balance factor and outputting a set of clusters for decision-making. Besides, we design the experiment to prove that Balanced COD-CLARANS works well. Keywords-data mining; constrained clustering; logistics distribution network; balance-driven; obstacle I. BACKGROUND AND PROBLEM STATEMENT With e-commerce develops rapidly in recent years, more and more people have already accepted and get used to go shopping online. Meanwhile, people also expect more convenient shopping experience, such as receiving goods as early as possible and so on. All these requirements cannot be satisfied without logistics distribution network, so it has been one of the main bottlenecks preventing enterprises and shops from further expanding their markets. Therefore, the optimization of logistics distribution network is of great importance and necessity. FIGURE I. THE DISTRIBUTION OF THE ORDERS IN SHANGHAI. For the purpose of the optimization of logistics distribution network, we focus on the problem of the siting of distribution stations, which is of key importance during designing a logistics distribution network. At present, express companies often decide the initial locations of distribution stations according to experience. As the situation changes, a station may be divided into several ones for overload, or merge with another because of underload. This process has no theory support and lacks replicability. Thus, it may cause waste of labor, material and money. Data mining is a hot topic in recent years. If an express company has a certain amount of geographical coordinates of orders and plans to change the stations' locations, or a online ecommerce platform having numerous geographical coordinates of orders wants to design its own logistics distribution network, both of them can mine useful “knowledge” through data mining to provide theories and references for the siting of distribution stations. Based on this idea, we obtain a dataset of the geographical coordinates of all orders of a certain express company per day in Shanghai and use clustering to get a set of stations' locations. We take the obstacles in real world into account and aim at not only making the orders as close as possible to their respective stations but also making the workload of each station as balanced as possible. The remainder of this paper is organized as follows. In Session 2, we summarize some related work. In Session 3, we introduce the algorithm called Balanced COD-CLARANS to optimize the logistics distribution network. Experimental results are analyzed in Section 4. Conclusions are given in Section 5.

Balanced COD-CLARANS: A Constrained Clustering Algorithm to Optimize Logistics Distribution Network