Research On The Optimization Of Supply Network Based On Customer Demand Under The Background Of Big Data

With the arrival of the era of big data, people’s living standard and life-style have been greatly changed. And people’s standard of living becomes higher and higher, so people ask higher requirement of logistics service level. As a result, the personalized need of customer and customer personal order have become a hot problem of research. Logistics service industry is the core of service industry, optimization of logistics service supply network, choosing a appropriate partner, improving customer satisfaction and reasonable allocation of tasks have become a hot topic at home and abroad. The logistics supply nework task allocation optimization problem about the customer’s personalized demand and anysis of user behavior has been studied, in this way, the satisfaction of customer and the competitiveness and benefits of enterprises will be improved.Firstly, related theories and methods of analysis of big data, mining technology, connotation characteristics and structure of big data are described in detail. The correlation theory and data mining method of customer’s individual needs and user’s behavior have been studied in the background of big data. The customer’s potential interest and behavior have been analyzed based on the customer’s online data and location data. Secondly, the influence of the user’s personalized demand on the logistics service supply network has been studied and the basic theory and the constuction of logistics network model are described in detail. The behavior of the user has been analyzed to predict the user’s best distribution time based on the user’s location data. Decision function for the attraction between customers and providers is built. And a model of order allocation in three-level logistics service supply chain with the objective of minimize the cost of logistics service integrator is established. The simulation results show the validity of the model. Finally, the optimization problem of logistics supply network about the individual demand of customers is considered, two decision methods about the personalized need of customer and the potential need of customer that is predicted based on the customer’s rating data are proposed under the situation of two kinds of products, and the prediction results are introduced into model of multi- level logistics network optimization. And 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 network. According to the characteristics of logistics supply network design problem, the combination of clustering mining with genetic algorithm and the combination of Apriori with genetic algorithm are used to solve the model respectively. The result shows that the decision-making method of choosing different customer need is an effective and feasible method to improve the service level of logistics network in different product context.The individual need and behavior analysis of customer are introduced to the model of the logistics service supply chain and logistics network optimization, which not only can help improve the level of service to the customer, but also can help manager timely understand potential interest of customer. So it can help decision-makers manage the logistics service enterprises betterly.