AI for cargo stowage
RoRo (Roll-on/Roll-off) vessels carry 234 million tonnes of goods annually around Europe, and is growing at an average rate of 3%.[1,2] RoRo vessels follow fixed routes between ports, and ferry cargo, mainly vehicles, across the sea. RoRo cargo arrives shortly before departure and is loaded in a hurry.
The task of the RoRo cargo stowage planner is to limit delays and to ensure a good balance of the cargo onboard for each trip. The stowage planners thus face a combinatorial explosion of potential arrangements. They must quickly coordinate loading, and determine a suitable arrangement for both placement and weight of cargo. Suboptimal arrangements are costing shipping companies millions per year, as a more balanced ship will provide fuel (and emissions) savings of up to 2% per trip.[3,4,5]
Commercial stowage planning software dedicated to RoRo ships already exist. They provide a visual aid for cargo planners, but fall short of providing decision support in terms of calculating stowage plan options. Using machine learning (ML) and deep learning (DL), Artificial Intelligence (AI) offers an algorithmic approach to support stowage optimization, calculating and suggesting options for the stowage planner. AI and digitalization also have the potential to provide a better service to clients by providing them with a more accurate time of when cargo will be discharged. Nevertheless, questions remain over the feasibility of integrating the technology.
When will AI be used to support the cargo stowage planner in RoRo operations?
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