Innovative Research from Pusan National University Enhances Vessel Management Efficiency at Ports

Pusan National University's Innovative Vessel Turnaround Time Model

Pusan National University (PNU) has embarked on a groundbreaking study aiming to alleviate the mounting pressure created by global port congestion. With increasing cargo volumes, it has become imperative for ports to optimize their operations. A team of researchers consisting of Professor Hyerim Bae and Master's student Daesan Park from the Department of Industrial Engineering has unveiled a dynamic forecasting framework that utilizes real-time operation indicators to enhance the prediction of vessel turnaround time (VTT).

In a rapidly evolving maritime industry, accurately determining the period between a vessel's arrival and departure is crucial. This timeframe directly influences scheduling, the management of congestion, and energy efficiency. Traditional forecasting methods have relied on static metrics such as vessel specifications and container volumes, which often miss the complexities of fluctuating port operations.

To address this challenge, the researchers have developed a model that harnesses a two-stage queuing methodology, producing a framework capable of improving VTT prediction accuracy by up to 28% compared to conventional techniques. This innovative approach captures the dynamic and interdependent nature of port systems, offering a more robust tool for planning and resource allocation.

The research team has integrated operation indicators – quantitative measures stemming from queue-based theory. These indicators consider key operational parameters like arrival rates and service rates at various stages, including berth and yard operations, thus facilitating a more comprehensive view of port activities. The model operates through time-series analysis, contrasting markedly with the static models that offer limited responsiveness to real-time changes in port conditions.

Once validated with data from Busan Port, the framework showcased significant improvements in forecast accuracy, leading to enhanced berth planning and reduced delays. Application of this model can optimize crane and truck allocation, streamline logistics, and minimize energy consumption – contributing significantly to operational efficiencies.

Professor Bae emphasizes the transformative potential of their research, articulating that it opens new avenues for extending the principles of this framework beyond maritime applications. For instance, similar predictive systems could be developed for other sectors, including aviation, healthcare, urban transportation, and manufacturing, where interdependencies are crucial for managing workflow and efficiency.

In airports, operations could utilize this model for predicting delays in aircraft handling, while hospitals could streamline patient flows from registration to treatment. Moreover, urban transport systems could analyze congestion spread, ensuring more responsive and adaptable travel routes. In manufacturing, the framework could help identify potential bottlenecks by mapping interdependencies between production lines.

As global trade continues to flourish and the need for operational efficiency heightens, the research from Pusan National University stands at the forefront of innovations seeking to enhance industry standards and practices.

Reference: Volume 69, Part B, Advanced Engineering Informatics, January 1, 2026. DOI: 10.1016/j.aei.2025.103974