In order to improve the construction of the assembly center, this paper builds an assembly center location optimization model with the smallest operation cost based on the composition of the cost of China Railway Express operation, and designs a multi-population genetic algorithm to solve it. Secondly, a transportation network composed of 20 cargo source areas, 3 alternative assembly centers, 4 ports, and 3 destination cities was selected as an example to conduct analysis and verification, determine the optimal location strategy. Finally, this article draws conclusions and suggestions, which can provide reference for the construction of China Railway Express assembly center.
In the context of the new era of rapid development of big data and artificial intelligence technologies, the use of data mining and algorithmic techniques to build sales prediction models with high accuracy rates can quickly help cross-border e-commerce (CBEC) enterprises to cope with the challenges related to data processing, feature extraction, and value information acquisition. Through the analysis and mining of raw data of CBEC transactions, a two-layer fusion model is built by using the stacking integrated learning method, with the Random Forest, XGBoost, and LightGBM algorithms as base learners and the support vector regression algorithm as a meta-learner, to predict CBEC sales and compare the prediction properties with the single model constructed by the base learner. The results show that the integrated learner trained by the stacking integrated learning method can improve the prediction performance of the single learner and performs best among all learners. The model can objectively reflect the status of CBEC sales and play an essential role in the company’s execution of strategic decisions such as merchandise sourcing and sales.
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