In this paper an example of speed down curve construction for one ship type is pre-sented. The relationship between ship’s speed changes and environmental factors is defined on the basis of observational data. The artificial neural network was used for a regression analysis. Taking into account the fact that input data imprecision can hardly be avoided, which consequently makes the predicted ship speed approximate, the proposed notation of ship speed in given conditions has the form of fuzzy number.

The article presents the ship weather routeing problem as a decision-making of ship route selection in fuzzy conditions. An attempt was made to set forth a formal defi-nition of fuzzy environment elements: fuzzy goals, constraints and decisions. The con-sidered problem was classified as a problem of decision made by one person, in one stage of decision procedure and as multi-criterion problem.

This paper deals with fuel consumption estimations relating to container ships on the basis of ship service and wave parameters. Data, on which to base estimations, was measured and recorded from a container ship during 96 months at sea. Approximating functions were calculated by the use of curve fitting techniques and regression methods, utilizing newly developed software named ndCurveMaster. The approximation function presented in this paper could have practical application for the estimation of container ship fuel consumption, while considering weather routing. In addition the study clearly shows the relationship between the fuel consumption of a container ship and the number of months since its last docking. These results may form the basis for further research in this direction.