Risk-Aware Stochastic Ship Routing Using Conditional Value-at-Risk

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

Risk-Aware Stochastic Ship Routing Using Conditional Value-at-Risk

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

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023, 00, pp. 10543-10550
Issue Date:: 2023-12-13

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Field	Value	Language
dc.contributor.author	Nuñez, A
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	2023-10-01
dc.date.accessioned	2024-03-25T07:53:44Z
dc.date.available	2024-03-25T07:53:44Z
dc.date.issued	2023-12-13
dc.identifier.citation	2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023, 00, pp. 10543-10550
dc.identifier.isbn	9781665491907
dc.identifier.issn	2153-0858
dc.identifier.issn	2153-0866
dc.identifier.uri	http://hdl.handle.net/10453/177123
dc.description.abstract	Improving the safety and efficiency of maritime shipping has the potential to reduce carbon emissions and improve profitability Stochastic ship routing the problem of finding a safe efficient and timely route for a ship is difficult in large part because of uncertainty in weather forecasts which come as an ensemble a collection of many possible future weather conditions Previous safety aware ship routing methods have used either conservative interpretations of the ensemble by assuming the worst case weather conditions leading to excessive fuel consumption or by using the average weather conditions leading to potentially unsafe routes In this paper we investigate the use of the well known Conditional Value at risk CVaR in the objective and constraint functions for ship routing problems which allows a range of risk tolerances between the average and the worst case We illustrate the advantages of using CVaR for the problem of ship routing in several simulation examples using real weather forecasts
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
dc.relation.ispartof	2023 IEEE/RSJ International Conference on Intelligent Robots and Systems
dc.relation.ispartofseries	IEEE International Conference on Intelligent Robots and Systems
dc.relation.isbasedon	10.1109/iros55552.2023.10341431
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Risk-Aware Stochastic Ship Routing Using Conditional Value-at-Risk
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Detroit, MI, USA
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/Strength - RI - Robotics Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-25T07:53:43Z
pubs.finish-date	2023-10-05
pubs.publication-status	Published
pubs.start-date	2023-10-01
pubs.volume	00

Abstract:

Improving the safety and efficiency of maritime shipping has the potential to reduce carbon emissions and improve profitability Stochastic ship routing the problem of finding a safe efficient and timely route for a ship is difficult in large part because of uncertainty in weather forecasts which come as an ensemble a collection of many possible future weather conditions Previous safety aware ship routing methods have used either conservative interpretations of the ensemble by assuming the worst case weather conditions leading to excessive fuel consumption or by using the average weather conditions leading to potentially unsafe routes In this paper we investigate the use of the well known Conditional Value at risk CVaR in the objective and constraint functions for ship routing problems which allows a range of risk tolerances between the average and the worst case We illustrate the advantages of using CVaR for the problem of ship routing in several simulation examples using real weather forecasts

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http://hdl.handle.net/10453/177123