Fuel-saving route planning with data-driven and learning-based approaches: A systematic solution for harbor tugs

• Main Authors: WANG, Shengming, ZHANG, Xiaocai, LI, Jing, WEI, Xiaoyang, LAU, Hoong Chuin, DAI, Bing Tian, HUANG, Binbin Huang, XIAO, Zhe, FU, Xiuju, QIN, Zheng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Artificial Intelligence and Robotics; Operations Research, Systems Engineering and Industrial Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/9364; https://ink.library.smu.edu.sg/context/sis_research/article/10364/viewcontent/0828_pvoa.pdf
• Institution: Singapore Management University

In recent years, there are trends toward cleaner port environments through enforcement by imposed legislation. Transit optimisation of fuel-based port service boats like harbour tugs has emerged as a critical task to reduce fuel consumption and carbon emission. In this paper, an innovative learning-based method, comprising a Reinforcement Learning (RL) model together with a fuel consumption prediction model, was proposed to formulate fuel-saving transit routes. Firstly, an ensemble model is established by combining a Long Short-Term Memory (LSTM) model with a Multilayer Perceptron (MLP) model, predicting fuel use based on tugboat movement and environment factors. Subsequently, an innovative RL based on Deep Deterministic Policy Gradient (DDPG) framework is developed considering the characteristics and obstructions of waterway in Singapore as well as the environmental factors to learn the optimal transit strategy that minimizes fuel consumption. We also demonstrate the efficacy of the solution to generate routes from origin to destination terminals, exhibiting significantly reduced fuel consumption in comparison to real-world transit scenarios.