Route optimization tool for vessels in ice-covered waters complying with carbon intensity index regulatory constraint

Route planning plays an integral part of each voyage in maritime operations. The selected route must be optimized in terms of economic factors and safety concerns. It also has to adhere to national and international regulations. Recently, the International Maritime Organization introduced a new regulatory instrument to promote the
decarbonization of the shipping industry. The regulatory instrument is called the carbon intensity index (CII). This project aims to solve route planning where all economic objectives are optimized while the operation adheres to the new CII regulation. The proposed method uses an Artificial Intelligence method called Reinforcement Learning
to formulate this issue. In the model, the vessel is an agent that explores the ice-covered environment to search for the best routes. A system of reward signals is defined to make sure the solutions achieve the optimality where their operation meets the CII constraint. This model will be tested in a simulation environment using a realistic voyage scenario in the Canadian Arctic.

Faculty Supervisor:

Brian Veitch

Student:

Partner:

Springboard Atlantic Inc.

Discipline:

Engineering

Sector:

Ocean Tech; Transportation (excluding aerospace); Artificial Intelligence

University:

Memorial University of Newfoundland

Program: