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

• Main Authors: WANG, Shengming, ZHANG, Xiaocai, JING, Li, WEI, Xiaoyang, LAU, Hoong Chuin, DAI, Bing Tian, HUANG, Binbin, ZHE, Xiao, FU, Xiuju, ZHENG, Qin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Reinforcement Learning model; Fuel consumption prediction; Transit route optimisation; Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/sis_research/9922
• 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 learningb...