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How Artificial Intelligence can Make Our Port Smarter



Various factors have fuelled this progress. One of which is improvement in computer processing power. AI can now tackle complex real-world problems that cannot be solved within a reasonable amount of time previously. Exponential growth rate in data generation and availability – thanks to the advent of Internet of Things (IoT) – is yet another driver. That is why there are increasing demands for AI to make sense of data and use them to make better decisions. 




Like many other industries, AI is exerting an increasing infuence on PSA’s port business operations. However, to better understand the impact of AI on the operational efficiency of the Singapore container terminal, we need to first understand what is involved in container terminal operations. Particularly, container handling for vessels constitutes a significant part of the container terminal operations. 


Upon a vessel’s arrival at a berth in the container terminal, containers are discharged from and loaded onto it. A typical discharging operation starts with a quay crane picking up a container from the vessel and placing it onto a prime mover, which will then move to a storage yard. At the storage yard, a yard crane picks up the container from the prime mover and shifts it to a designated spot. Loading operations involve the movement of containers in the opposite direction, i.e. from the yard to the vessel. 





Notably, three major decisions need to be made during a discharging operation, namely selecting which prime mover will transport the container from the quay crane to the yard, identifying which storage yard to send the prime mover (together with the container) to, and assigning a yard crane to serve the prime mover when it arrives at the yard. 






With the help of AI, not only can the estimated waiting time of the prime mover at various locations in the container terminal be better predicted, the best prime mover can also be deployed to pick up the container. Subsequently, AI conducts a real-time search by taking into consideration the waiting time of the prime mover at the yard and whether the container will eventually be trucked out or loaded onto another vessel before sending the prime mover and the container to an appropriate yard. 


Finally, when the prime mover approaches the yard, AI assigns a suitable yard crane that is ready to serve the prime mover when it reaches the yard. For this to happen, a prediction model is used to determine the locations of the prime mover and the yard cranes around the storage yard that the prime mover is heading to. This is followed by an optimisation process which allocates a yard crane to the approaching prime mover. 


Similarly, AI is also applied to loading operations, yard housekeeping operations, and various planning processes. These AI-powered decisions contribute towards the overall efficiency of container handling operations by enabling the transportation of containers between vessels and storage yards using three different sets of equipment – quay cranes, prime movers, and yard cranes – to be carried out seamlessly. These repeated operations over tens of millions of times a year means that any minute time and cost savings per operation is magnified a million-fold! 




Forging ahead, as PSA prepares to move all container terminal operations to the Tuas terminal, it is foreseeable that AI will play a more pivotal role in fuelling the fully automated terminal. For one, AI will drive the automated guided vehicles (AGVs), which will replace the current prime movers, as if they were driven by humans – or even better than humans. Likewise, the quay cranes and yard cranes will also be replaced by their automated counterparts.  


With humans removed from the container terminal operations equation, we can expect more certainty in the way operations are conducted, paving the way for a more intelligent system that can orchestrate and synchronise different automated equipment. The road ahead for AI sure looks to be filled with endless possibilities! 


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