Use of Artificial Intelligence as a Problem Solution for Maritime Transport

Abstract

The purpose of this article is to propose a solution for the transport problem in sea freight using machine learning algorithms. An important aspect of sea transport is the organization of freight. In particular, the maritime freight network is a large complex system whose complexity of route maps and the variety of ship traffic render it difficult to model. When investigating the characteristics of the sea freight system, it is generally advisable to use rough models in which only significant approximations are introduced and a number of details are not taken into account. At the same time, an exact model is used in a detailed study of isolated areas of the network wherein it is the area which is explored in detail and not the connections between the said areas. By so doing, one should be careful not to overlook the deviations of the model from the real network in the first case and the connections between areas in the second.Building a model that accurately takes into account and describes all the details results in excessive complications in the design process, so, in practice, a number of assumptions are always used in the simulation which are basically approximations of the real characteristics related to ship movement, depending on the specific task. Four models are used in order to build an optimal cargo transportation system: Transnational cargo model; model of cargo transportation with a dedicated initial port of cargo departure; model of cargo transportation with dedicated initial ports of departure and final port of cargo distribution; model of cargo transportation on a circular chain of ports. The route conditions are given by the traveling wave equation and on the basis of these calculations the optimal route of cargo ship movement is put forth whereby conditions affecting freight traffic include: Number of ports, fuel quantity, port of cargo destination, as well as distances between ports and intermediate ports of call. The scientific contribution lies in the fact that the human role is reduced only to that of the system observer, which, in turn, simplifies the freight calculations, as well as helps reduce the cost of fuel and human resources.

Keywords

• transport problem
• port
• traveling wave equation
• optimization
• machine learning