{"type":"ScholarlyArticle","title":"Fuel-Saving Route Planning with Data-Driven and Learning-Based Approaches – A Systematic Solution for Harbor Tugs","authors":[{"name":"Shengming Wang","slug":"shengming-wang","given":"Shengming","family":"Wang","affiliation":"Institute of High Performance Computing , Agency for Science, Technology and Research , Singapore"},{"name":"Xiaocai Zhang","slug":"xiaocai-zhang","given":"Xiaocai","family":"Zhang","affiliation":"Institute of High Performance Computing , Agency for Science, Technology and Research , Singapore"},{"name":"Jing Li","slug":"jing-li","given":"Jing","family":"Li","affiliation":"Institute of High Performance Computing , Agency for Science, Technology and Research , Singapore; Centre for Frontier AI Research , Agency for Science, Technology and Research , Singapore"},{"name":"Xiaoyang Wei","slug":"xiaoyang-wei","given":"Xiaoyang","family":"Wei","affiliation":"Institute of High Performance Computing , Agency for Science, Technology and Research , Singapore"},{"name":"Hoong Chuin Lau","slug":"hoong-chuin-lau","given":"Hoong Chuin","family":"Lau","affiliation":"Institute of High Performance Computing , Agency for Science, Technology and Research , Singapore; School of Computing and Information Systems , Singapore Management University"},{"name":"Bing Tian Dai","slug":"bing-tian-dai","given":"Bing Tian","family":"Dai","affiliation":"School of Computing and Information Systems , Singapore Management University"},{"name":"Binbin Huang","slug":"binbin-huang","given":"Binbin","family":"Huang","affiliation":"School of Computer Science and Technology , Hangzhou Dianzi University {wang shengming ,"},{"name":"Zhe Xiao","slug":"zhe-xiao","given":"Zhe","family":"Xiao","affiliation":"Institute of High Performance Computing , Agency for Science, Technology and Research , Singapore"},{"name":"Xiuju Fu","slug":"xiuju-fu","given":"Xiuju","family":"Fu","affiliation":"Institute of High Performance Computing , Agency for Science, Technology and Research , Singapore"},{"name":"Zheng Qin","slug":"zheng-qin","given":"Zheng","family":"Qin","affiliation":"Institute of High Performance Computing , Agency for Science, Technology and Research , Singapore"}],"abstract":"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.","doi":"10.24963/ijcai.2024/828","url":"https://ijcai.org/proceedings/2024/828","pdf":"https://ijcai.org/proceedings/2024/0828.pdf","markdown":"https://ijcai.org/proceedings/2024/828.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/828","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI for Good","track":"AI for Good","number":828,"pages":{"first":7483,"last":7490},"keywords":["Agent-based and Multi-agent Systems: General","Data Mining: General","Multidisciplinary Topics and Applications: General","Planning and Scheduling: General"],"videos":[],"references":[{"position":1,"raw":"[Alahi et al., 2016] Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Sil- vio Savarese. 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