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dc.contributor.authorLee, Habin*
dc.contributor.authorAydin, N.*
dc.contributor.authorChoi, Y.*
dc.contributor.authorLekhavat, S.*
dc.contributor.authorIrani, Zahir*
dc.date.accessioned2017-12-08T10:10:58Z
dc.date.available2017-12-08T10:10:58Z
dc.date.issued2018-10
dc.identifier.citationLee H, Aydin N, Choi Y et al (2018) A decision support system for vessel speed decision in maritime logistics using weather archive big data. Computers and Operations Research. 98: 330-342.en_US
dc.identifier.urihttp://hdl.handle.net/10454/14121
dc.descriptionYesen_US
dc.description.abstractSpeed 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.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1016/j.cor.2017.06.005en_US
dc.rights© 2017 The Authors. This is an Open Access article distributed under the Creative Commons CC-BY license (http://creativecommons.org/licenses/by/4.0/)en_US
dc.subjectSpeed optimisation; Sustainable martime logistics; Weather archive data; Liner shipping; Particle swarm optimisationen_US
dc.titleA decision support system for vessel speed decision in maritime logistics using weather archive big dataen_US
dc.status.refereedYesen_US
dc.date.Accepted2017-06-04
dc.date.application2017-06-13
dc.typeArticleen_US
dc.type.versionPublished versionen_US
refterms.dateFOA2018-07-29T03:25:58Z


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