Risk-aware stochastic ship routing using constrained continuous belief tree search

Nuñez, A; Wakulicz, J; Kong, FH; González-Cantos, A; Fitch, R

Risk-aware stochastic ship routing using constrained continuous belief tree search

Nuñez, A Wakulicz, J Kong, FH González-Cantos, A Fitch, R

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Ocean Engineering, 2024, 314
Issue Date:: 2024-12-15

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Field	Value	Language
dc.contributor.author	Nuñez, A
dc.contributor.author	Wakulicz, J
dc.contributor.author	Kong, FH
dc.contributor.author	González-Cantos, A
dc.contributor.author	Fitch, R https://orcid.org/0000-0003-2215-5188
dc.date.accessioned	2025-01-24T05:25:28Z
dc.date.available	2025-01-24T05:25:28Z
dc.date.issued	2024-12-15
dc.identifier.citation	Ocean Engineering, 2024, 314
dc.identifier.issn	0029-8018
dc.identifier.issn	1873-5258
dc.identifier.uri	http://hdl.handle.net/10453/184197
dc.description.abstract	Improved route planning for commercial shipping can enable reduced environmental impact, improve ship safety records, and lower fuel and maintenance costs. A fundamental challenge is to design ship routing algorithms that can contend with uncertain weather forecasts and real-world models of ship performance and safety. This paper introduces a stochastic ship routing framework that uses the conditional value-at-risk (CVaR) metric to guide the behaviour of a modified Continuous Belief Tree Search (CBTS) algorithm to find a safe and fuel-efficient long-haul shipping route. Our method provides a principled means to utilise a probabilistic representation of weather forecasts derived from ensemble forecasting for the purpose of route planning and allows for a user-defined threshold of risk tolerance. Another key advantage of our method is its ability to dynamically choose candidate route waypoints using weather-dependent estimates of fuel and safety information. Evaluation of long-haul routes through the Atlantic, Pacific and Indian oceans using real-world ship performance models and weather forecasts show considerable improvements in fuel usage and computation time compared to state-of-the-art ship routing algorithms.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Ocean Engineering
dc.relation.isbasedon	10.1016/j.oceaneng.2024.119581
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0405 Oceanography, 0905 Civil Engineering, 0911 Maritime Engineering
dc.subject.classification	Civil Engineering
dc.subject.classification	4005 Civil engineering
dc.subject.classification	4012 Fluid mechanics and thermal engineering
dc.subject.classification	4015 Maritime engineering
dc.title	Risk-aware stochastic ship routing using constrained continuous belief tree search
dc.type	Journal Article
utslib.citation.volume	314
utslib.for	0405 Oceanography
utslib.for	0905 Civil Engineering
utslib.for	0911 Maritime Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Robotics Institute (RI)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Advanced Manufacturing (CAM)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Advanced Manufacturing (CAM)/Centre for Advanced Manufacturing (CAM) Associate Members
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc/4.0/
dc.date.updated	2025-01-24T05:25:27Z
pubs.publication-status	Published
pubs.volume	314

Abstract:

Improved route planning for commercial shipping can enable reduced environmental impact, improve ship safety records, and lower fuel and maintenance costs. A fundamental challenge is to design ship routing algorithms that can contend with uncertain weather forecasts and real-world models of ship performance and safety. This paper introduces a stochastic ship routing framework that uses the conditional value-at-risk (CVaR) metric to guide the behaviour of a modified Continuous Belief Tree Search (CBTS) algorithm to find a safe and fuel-efficient long-haul shipping route. Our method provides a principled means to utilise a probabilistic representation of weather forecasts derived from ensemble forecasting for the purpose of route planning and allows for a user-defined threshold of risk tolerance. Another key advantage of our method is its ability to dynamically choose candidate route waypoints using weather-dependent estimates of fuel and safety information. Evaluation of long-haul routes through the Atlantic, Pacific and Indian oceans using real-world ship performance models and weather forecasts show considerable improvements in fuel usage and computation time compared to state-of-the-art ship routing algorithms.

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http://hdl.handle.net/10453/184197