Optimization for Logistics Network Based on the Demand Analysis of Customer

In order to solve the problem that the customer's personalized need are not fully considered in the existing multi-level logistics distribution network, the combination of customer's potential demand with the customer's personalized need that introduced into the logistics network is proposed. According to the customer's network score data to predict the potential need of customer, and combined with the customer's personalized need to select the best provider to offer service. A multi-level logistics supply network optimization model with constraints of distribution capacity, inventory capacity and customer's best delivery time is built whose optimization objective is the sum of service costs, inventory costs and transportation costs of the whole logistics supply chain network. According to the characteristics of model, the combination of genetic algorithm with clustering mining and Apriori is used to solve the model. The result shows that the method of customer personalized need to optimize the logistics network is contribute to improve customer's satisfaction, and it is an effective and feasible method to improve the service level of logistics network.