A Decomposed-Ensemble Prediction Framework for Gate-In Operations at Container Terminals

Container terminals play a crucial role in global logistics and trade, with gate-in operations significantly impacting overall terminal efficiency and cargo turnover speed. This paper analyzes a series of problems caused by the randomness of the arrival of export containers at the container yard, including wastage of yard space, excessive waiting time for external trucks, and conflicts with other production operations. To address these issues, a method based on a decomposed ensemble framework is proposed to predict short-term container quantities for gate-in operations at container terminal gates. The experiment compares the autoregressive integrated moving average (ARIMA) algorithm, the prophet algorithm, and the Long Short-Term Memory (LSTM) algorithm, with results indicating the clear advantage of Long Short-Term Memory in decomposed time series modeling. The introduction of this method is expected to enhance the accuracy and flexibility of terminal production planning, optimizing resource utilization. Contributions of this paper include the proposal of predictive models, a shipping route-based decomposed-ensemble framework, and confirmation of the superiority of Long Short-Term Memory in prediction through comparative analysis. These contributions are expected to improve terminal operational efficiency, reduce resource wastage, and better adapt to the highly stochastic gate-in operation environment.