Recent studies in maritime shipping have concentrated on environmental and economic impacts of ships. In this regard, fuel is considered as one of the important factors for such impacts. In this regard, optimal bunkering scheduling is an important subject. The concept of “first-come first serve” rule at the ports for the bunkering operations causes many vessels to spend hours or sometimes days outside of the port waiting to be served. However, with an optimal scheduling management system, all upcoming vessels will reach the port in a suitable time bracket, making the waiting time as low as possible.
We solved this problem by developing optimization algorithms and efficient machine learning methods to optimize the bunkering scheduling process at the ports. Our machine learning models can estimate the Earliest Time of Arrival (EPTA) of any vessel, given their real-time position, weather condition, and type of the vessel. This estimation, commonly known as “Calculated Time of Arrival” is way more reliable than the traditional “Estimated Time of Arrival” (ETA) which is manually reported by the upcoming vessels. Having EPTA, our developed optimization framework provides an optimal scheduling program for the upcoming vessels, which leads to minimum waiting time and idle time of all vessels during the bunkering process.
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