Massive-scale graph mining for e-commerce cold chain analysis and optimization

Abstract Presently, the development of fresh agricultural products e-commerce is constrained by the cold chain logistics. There are shortcomings such as the incomplete comprehensive supporting system, lacking of logistics technology and low effective infrastructure in cold chain logistics. Scientific and optimal selection of distribution mode is crucial to the operation of fresh e-commerce and urban circulation efficiency. Owing to the problem that urban logistics distribution limits the cold chain logistics of fresh agricultural products, we propose the distribution mode and synergistic mechanism of cold chain logistics of urban fresh agricultural products. By leveraging the theory of logistics and supply chain management, we explore the high logistics cost of fresh e-commerce, inconsistent cold chain logistics standards, and poor consumer viscosity. We introduce the self-established logistics, third-party logistics, self-established logistics, third-party logistics and other three main logistics distribution modes. Due to the impact of cold chain logistics distribution on the existing urban transportation, an integrated mode of self-established logistics/third-party logistics + joint distribution is designed. In order to promote the collaborative development of cold chain logistics distribution and fresh electricity, it is significant to establish three collaborative mechanisms from the perspective of government, enterprises and third-party logistics According to the green logistics, the carbon tax cost and cargo damage cost in the process of cold chain logistics distribution are discovered by a graphical model. The graphical model is constructed under the optimization objective of minimizing each cost. In order to avoid the problem that basic artificial fish swarm algorithm is prone to local optimum, a global artificial fish swarm algorithm is designed and solved. Extensive experimental results have shown the usefulness and effectiveness of our proposed method.