Machine Learning in Container Terminals
This paper sees Dr Leonard Heilig and Dr Eduardo Lalla-Ruiz of the University of Hamburg articulate the latest breakthroughs in machine learning.
With the new opportunities emerging from the current wave of digitalization in all parts of global logistics chains, terminal planning and management need to be revisited with a data-driven perspective. The amount of operational data, such as from a terminal operating system (TOS), together with data from a variety of new data sources, such as sensors and mobile technologies, growing fast, but mostly remains too under-processed or under-analyzed to be of real value.
Meanwhile, current projects and initiatives in the port industry indicate a growing interest in big data analytics solutions. One example of a big data analytics is the SAFER project of the Maritime and Port Authority of Singapore (MPA) in collaboration with IBM. Under the project, MPA has piloted three new analytics-based modules to improve management of Singapore's growing vessel traffic. Another example is the Navis ATOM Labs investigating the use of machine learning for the optimization and automation of terminal operations. In this technical paper, we provide a brief overview of potential applications of machine learning in container terminals and discuss challenges.
To establish a data-driven perspective on terminal planning and management, we analyzed the current state-of-the-art in academia regarding applications of Machine Learning (ML) in the context of container terminals. In the following, we briefly summarize the scope of those works within the operational areas of a container terminal.
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