Optimization Study of Sorting Center Volume Prediction Based on LSTM and Simulated Annealing Algorithm

This study focuses on the problem of cargo volume prediction and employee scheduling optimisation in the sorting centre of e-commerce logistics network. By establishing an LSTM-based cargo volume time series prediction model and a linear integer planning staff scheduling model, combined with an annealing simulation algorithm, accurate cargo volume prediction and staff scheduling optimisation are achieved in the sorting centre. The results show that effective cargo volume forecasting and staff scheduling strategies can significantly improve the operational efficiency and reduce the cost of e-commerce logistics network. This study provides effective decision support and guidance for the optimisation of e-commerce logistics network.