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dc.contributor.authorAydin, N-
dc.contributor.authorChoi, Y-
dc.contributor.authorLekhavat, S-
dc.contributor.authorIrani, Z-
dc.identifier.citationComputers and Operations Researchen_US
dc.description.abstractSpeed optimization of liner shipping 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 big 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 voyage data obtained from a liner companies in Turkey that has liner services in the Mediterranean and the Black Sea. 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 of the liner company and real world implications are discussed.en_US
dc.description.sponsorshipThis study was partially supported by Korea National Research Foundation through Global Research Network Program (Project no. 2016S1A2A2912265) and an EU Marie Skłodowska-Curie action funded project, MINI-CHIP, under grant number 611693.en_US
dc.subjectspeed optimizationen_US
dc.subjectsustainable maritime logisticsen_US
dc.subjectweather archive dataen_US
dc.subjectliner shippingen_US
dc.subjectparticle swarm optimizationen_US
dc.titleA decision support system for vessel speed decision in maritimes logistics using weather archive big dataen_US
dc.relation.isPartOfComputers and Operations Research-
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