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A decision support system for vessel speed decision in maritime logistics using weather archive big data

A decision support system for vessel speed decision in maritime logistics using weather archive big data
A decision support system for vessel speed decision in maritime logistics using weather archive big data
Speed optimization of liner vessels has significant economic and environmental impact for reducing fuel cost and Green House Gas (GHG) emission as the shipping over maritime logistics takes more than 70% of world transportation. While slow steaming is widely used as best practices for liner shipping companies, they are also under the pressure to maintain service level agreement (SLA) with their cargo clients. Thus, deciding optimal speed that minimizes fuel consumption while maintaining SLA is managerial decision problem. Studies in the literature use theoretical fuel consumption functions in their speed optimization models but these functions have limitations due to weather conditions in voyages. This paper uses weather archive data to estimate the real fuel consumption function for speed optimization problems. In particular, Copernicus data set is used as the source of big data and data mining technique is applied to identify the impact of weather conditions based on a given voyage route. Particle swarm optimization, a metaheuristic optimization method, is applied to find Pareto optimal solutions that minimize fuel consumption and maximize SLA. The usefulness of the proposed approach is verified through the real data obtained from a liner company and real world implications are discussed.
Liner shipping, Particle swarm optimization, Speed optimization, Sustainable maritime logistics, Weather archive data
0305-0548
330-342
Lee, Habin
bab650b0-df62-40c1-bb0e-53d778ade29d
Aydin, Nursen
c315dbe9-1c70-49a2-8a15-ceff9ca283fd
Choi, Youngseok
928c489e-7c5b-42fc-bad8-77ce717ba106
Lekhavat, Saowanit
ce7cc0ed-3c0c-43ca-9bc4-749e7223f6b8
Irani, Zahir
a7517c03-0c1d-49a1-9973-1d4c5b2917f0
Lee, Habin
bab650b0-df62-40c1-bb0e-53d778ade29d
Aydin, Nursen
c315dbe9-1c70-49a2-8a15-ceff9ca283fd
Choi, Youngseok
928c489e-7c5b-42fc-bad8-77ce717ba106
Lekhavat, Saowanit
ce7cc0ed-3c0c-43ca-9bc4-749e7223f6b8
Irani, Zahir
a7517c03-0c1d-49a1-9973-1d4c5b2917f0

Lee, Habin, Aydin, Nursen, Choi, Youngseok, Lekhavat, Saowanit and Irani, Zahir (2018) A decision support system for vessel speed decision in maritime logistics using weather archive big data. Computers and Operations Research, 98, 330-342. (doi:10.1016/j.cor.2017.06.005).

Record type: Article

Abstract

Speed optimization of liner vessels has significant economic and environmental impact for reducing fuel cost and Green House Gas (GHG) emission as the shipping over maritime logistics takes more than 70% of world transportation. While slow steaming is widely used as best practices for liner shipping companies, they are also under the pressure to maintain service level agreement (SLA) with their cargo clients. Thus, deciding optimal speed that minimizes fuel consumption while maintaining SLA is managerial decision problem. Studies in the literature use theoretical fuel consumption functions in their speed optimization models but these functions have limitations due to weather conditions in voyages. This paper uses weather archive data to estimate the real fuel consumption function for speed optimization problems. In particular, Copernicus data set is used as the source of big data and data mining technique is applied to identify the impact of weather conditions based on a given voyage route. Particle swarm optimization, a metaheuristic optimization method, is applied to find Pareto optimal solutions that minimize fuel consumption and maximize SLA. The usefulness of the proposed approach is verified through the real data obtained from a liner company and real world implications are discussed.

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Accepted/In Press date: 4 June 2017
e-pub ahead of print date: 13 June 2017
Published date: 1 October 2018
Keywords: Liner shipping, Particle swarm optimization, Speed optimization, Sustainable maritime logistics, Weather archive data

Identifiers

Local EPrints ID: 437726
URI: http://eprints.soton.ac.uk/id/eprint/437726
ISSN: 0305-0548
PURE UUID: c81467bb-d78c-4291-be3f-f82164188fef
ORCID for Youngseok Choi: ORCID iD orcid.org/0000-0001-9842-5231

Catalogue record

Date deposited: 13 Feb 2020 17:30
Last modified: 26 Nov 2021 03:21

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Contributors

Author: Habin Lee
Author: Nursen Aydin
Author: Youngseok Choi ORCID iD
Author: Saowanit Lekhavat
Author: Zahir Irani

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