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

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journal contribution
posted on 19.04.2018 by Habin Lee, Nursen Aydin, Youngseok Choi, Saowanit Lekhavat, Zahir Irani
© 2017. 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.


This 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.



  • Business and Economics


  • Business

Published in

Computers and Operations Research


LEE, H. ...2018. A decision support system for vessel speed decision in maritime logistics using weather archive big data. Computers and Operations Research, 98, pp.330-342.


© The Authors. Published by Elsevier


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This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/

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This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/