Operational performance evaluation of a container terminal using data mining and simulation

Abstract

The efficient operation of container terminals facilitates the seamless flow of goods across borders. New technologies such as big data, data mining, and simulation models, have emerged in the maritime industry, enabling optimization and performance evaluation. This study investigated how data science using operational data can improve container terminal operations, drive efficiency, enhance throughput, and bolster competitiveness in the shipping sector. Decision-making within container terminals, particularly in determining optimal container stacking locations, poses a significant challenge owing to the multitude of factors at play. By analyzing the datasets, new strategies and policies can be simulated to minimize container rehandling operations. This study focuses on the Hakata International Container Terminal in Japan, where daily operational data from rubber-tired gantry equipment are used to investigate container movements. A significant reduction in the total number of movements required to perform container-handling operations was demonstrated by implementing data-driven strategies and simulation modeling.