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Container shipping plays a pivotal role in global trade, and understanding the duration that vessels spend in ports is crucial for efficient voyage planning by shipping companies. However, these companies often rely solely on one-way communication for required arrival times provided by terminals. This reliance on fixed schedules can lead to vessels arriving punctually, only to face berths that are still occupied, resulting in unnecessary waiting times. Regrettably, limited attention has been given to these issues from the perspective of shipping companies. This study addresses this gap by focusing on the estimation of dwell times for container vessels at a terminal in the Port of Busan using various machine learning techniques. The estimations were compared against the terminal's operational reference. To compile the dataset, a 41-month history of terminal berth schedules and vessel particulars data were utilized and preprocessed for effective training. Outliers were removed, and dimensions were reduced. Six regression machine learning algorithms, namely adaptive learning, gradient boosting, light gradient boosting, extreme gradient boosting, categorical boosting and random forest, were employed, and their parameters were fine-tuned for optimal performance on the validation dataset. The results indicated that all models exhibited superior performance compared to the terminal's operating reference model. [ABSTRACT FROM AUTHOR]

This study proposes a systematical approach to evaluate the operational capability of container terminals and discusses the effect of resource usages on operational performances. Two inter-dependent processes (i.e. the loading-discharging (L&D) and the delivery-receiving (D&R) operational processes) with shared/non-shared resources and common/separate productions are examined and characterized as a two-stage parallel network. An evaluation model is developed upon the principles of data envelopment analysis (DEA) to assess the operational capability of the terminals. Using the real-world dataset of 9 container terminals at Port of Busan, comparative performance results are obtained for 5 years spanning across 2014–2018. The proposed model demonstrates a much stronger discriminative power compared to the traditional CCR model in its estimations of performance in the decision-making units (DMUs). It can also be inferred from the results that efficiency in operations is a key qualifier for container volume while the market aggressiveness lends a competitive edge and reinforces a positive outcome on the performance of a container terminal. The study further examines the influence of management directive on a terminal performance and confirms that alignment of management directive with the operational capability of L&D and D&R processes is important in maximizing terminal throughout. [ABSTRACT FROM AUTHOR]