A machine learning based Bayesian decision support system for efficient navigation of double-ended ferries  

作　　者：Vergara Daniel Alexandersson Martin Lang Xiao Mao Wengang 

机构地区：[1]Department of Mechanics and Maritime Sciences,Chalmers University of Technology,Sweden [2]SSPA,Research Institutes of Sweden,Gothenburg,Sweden

出　　处：《Journal of Ocean Engineering and Science》2024年第6期605-615,共11页海洋工程与科学（英文）

基　　金：support from the Swedish Foundation for International Cooperation in Research and Higher Education(CH2016–6673).

摘　　要：Ships can be operated more efficiently by utilizing intelligent decision support integrated with onboard data collection systems.In this study,a Bayesian optimization-based decision support system,which uti-lizes ship performance models built by machine learning methods,is proposed to help determine the operational set-points of two engines for double-ended ferries.By optimizing the ferries’power alloca-tion between the stern and bow engines,the Decision Support System(DSS)will simultaneously attempt to keep the ETA of the ferry fixed under a set of operational constraints using the Bayesian optimization.Its objective is to minimize fuel consumption along individual trips.Based on simulation environment,the DSS can reduce at maximum 40%fuel consumption with no significant change of the ETA.Final full-scale experiments of a double-ended ferry demonstrated an average of 15%,where at least half of this saving was achieved by the optimized power allocation between bow and stern engines.

关 键 词：Bayesian optimization Energy efficiency Ship navigation Machine learning ship models 

分 类 号：U662[交通运输工程—船舶及航道工程]