Intelligent scheduling algorithm and big data analysis for cross-border e-commerce supply chain optimization

Abstract

This paper focuses on the optimization of cross-border e-commerce supply chain, and proposes an innovative method that integrates intelligent scheduling algorithm and big data analysis. Cross-border e-commerce supply chain faces many challenges, such as global logistics complexity, demand fluctuation, data island and so on, because it involves complex links such as transnational logistics, multi-currency settlement and customs clearance. In order to solve these problems, an algorithm framework including data integration layer, algorithm core layer and application layer is constructed. The data integration layer builds a unified data lake based on Delta Lake to collect multi-source heterogeneous data, The core layer of the algorithm adopts hierarchical reinforcement learning (RL) architecture, which covers markov decision processes modeling, dynamic constraint satisfaction optimization and real-time feedback control. The application layer realizes the integrated processing of streams and batches through Flink, and supports a variety of application scenarios. In the design of intelligent scheduling algorithm, LSTM-Transformer hybrid model is used for demand forecasting, logistics path optimization is realized by multi-objective function, and Q-learning algorithm is used for inventory scheduling. The key technologies of big data analysis include using Isolation Forest algorithm to build a dynamic threshold model for real-time anomaly detection, and using GraphSAGE technology for multimodal data fusion. The experiment uses Ali Tianchi cross-border e-commerce data set and related external data sources. The results show that this research method is superior to the traditional method in terms of demand forecasting accuracy, logistics optimization effect and anomaly detection accuracy, which significantly improves key indicators such as order fulfillment cycle, inventory turnover rate, logistics cost ratio and customer satisfaction, and hierarchical RL algorithm shows superior performance in solving large-scale complex problems.

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