Prediction of a Newbuilding Proce of the Bulk Carriers based on Gross Tonnage GT and Main Engine Power

Abstract

The paper presents mathematical relationships that allow us to forecast the newbuilding price of new bulk carriers, based on data concerning vessels built in 2005-2015. The presented approximations allow us to estimate the price based on a gross tonnage capacity and a main engine power The approximations were developed using linear regression and the theory of artificial neural networks. The presented relations have practical application for estimation of bulk carrier newbuilding price needed in preliminary parametric design of the ship. It follows from the above that the use of artificial neural networks to predict the price of a bulk carrier brings more accurate solutions than linear regression.

Keywords

• bulk carrier
• newbuilding price
• cost estimation
• gross tonnage
• engine power
• design
• artificial neural networks
• linear regression
• approximation