Reinforcement learning-based autonomous generation technology for product oil tanker loading schemes

Abstract

ObjectiveThis paper focuses on using reinforcement learning-based automatic generation technology to generate loading and unloading schemes for the liquid cargo tanks of oil tankers. MethodsUsing the cargo capacity of an actual operating oil tanker as the input and the loading rates of the cargo tank and ballast water tank as the targets, an intelligent agent and environment are built based on Unity ML-Agents. The agent is trained using the PyTorch framework, and a reward function calculation method that comprehensively considers the loading time and changes in the trim amplitude is proposed. Finally, example analysis is carried out to validate the feasibility of the proposed method. ResultsThe results show that the trained agent can learn effective strategies for achieving the autonomous generation of liquid cargo tank loading schemes. ConclusionsThis study proves that it is reasonable and feasible to apply reinforcement learning to solve the problem of the autonomous generation of liquid cargo tank loading schemes under multi-objective conditions.

Keywords

• automatic loading and unloading
• liquid cargo tank
• machine learning
• scheme optimization