Optimization of maritime routes for a more efficient, safer and decarbonized transport

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IE University and Universidad de Cádiz promote this interdisciplinary project aimed at providing algorithms to calculate safe and optimized shipping routes for reduced fuel consumption and emissions, taking into account real time oceanographic and meteorological information. The necessary data will be collected from public repositories such as NOAA, Copernicus or HYCOM, and they include predictions for ocean currents, wind and waves. Detailed models will be built to estimate the fuel consumption and emissions of every vessel as a function of its characteristics (displacement, hull length, engine power), cruising speed and sea conditions (currents, waves and wind).

This task involves both reviewing the vast literature on this subject and possibly fitting our own data driven models in case routes and consumption data become available. Next, optimization algorithms will be developed to find optimal routes and calculate fuel savings and estimated time of arrival (ETA) at the port destination. Profiting from better sea and weather conditions has been shown to save on average 3-7% of fuel. This is a challenging project whose success depends on the cross fertilization and cooperation between oceanographers, naval engineers, data scientists, mathematicians and software developers.

The goal is both to improve the state of the art algorithms for weather routing, publishing our results in specialized scientific journals and to develop software tools that address this important industrial problem for shipping companies.

Optimization of maritime routes for a more efficient, safer and decarbonized transport - Weather Navigation

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This research is supported by:

  • BBVA Foundation via the project “Mathematical optimization for a more efficient, safer and decarbonized maritime transport”.

  • Spanish Agencia Estatal de Investigación under grant TED2021-129455B-I00, “Optimization of maritime routes with real time oceanographic and meteorological data”.

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