Toward Reducing Unproductive Container Moves: Predicting Service Requirements and Dwell Times
This work addresses operational efficiency for container terminal logistics, but it is incremental as it applies existing predictive methods to a specific domain problem.
The study tackled the problem of unproductive container moves at a container terminal by developing machine learning models to predict service requirements and dwell times, resulting in models that consistently outperformed rule-based heuristics and random baselines in precision and recall across multiple validation periods.
This article presents the results of a data science study conducted at a container terminal, aimed at reducing unproductive container moves through the prediction of service requirements and container dwell times. We develop and evaluate machine learning models that leverage historical operational data to anticipate which containers will require pre-clearance handling services prior to cargo release and to estimate how long they are expected to remain in the terminal. As part of the data preparation process, we implement a classification system for cargo descriptions and perform deduplication of consignee records to improve data consistency and feature quality. These predictive capabilities provide valuable inputs for strategic planning and resource allocation in yard operations. Across multiple temporal validation periods, the proposed models consistently outperform existing rule-based heuristics and random baselines in precision and recall. These results demonstrate the practical value of predictive analytics for improving operational efficiency and supporting data-driven decision-making in container terminal logistics.