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A decision support system for vessel speed decision in maritime logistics using weather archive big data
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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 & Operations Research, 98 . pp. 330-342. doi:10.1016/j.cor.2017.06.005 ISSN 0305-0548.
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Official URL: http://dx.doi.org/10.1016/j.cor.2017.06.005
Abstract
Speed 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.
| Item Type: | Journal Article | ||||||||
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| Subjects: | H Social Sciences > HE Transportation and Communications | ||||||||
| Divisions: | Faculty of Social Sciences > Warwick Business School | ||||||||
| Library of Congress Subject Headings (LCSH): | Cargo ships -- Fuel consumption -- Mathematical models, Shipping, Big data, Weather | ||||||||
| Journal or Publication Title: | Computers & Operations Research | ||||||||
| Publisher: | Elsevier BV | ||||||||
| ISSN: | 0305-0548 | ||||||||
| Official Date: | October 2018 | ||||||||
| Dates: |
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| Volume: | 98 | ||||||||
| Page Range: | pp. 330-342 | ||||||||
| DOI: | 10.1016/j.cor.2017.06.005 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||
| Date of first compliant deposit: | 15 June 2017 | ||||||||
| Date of first compliant Open Access: | 26 June 2017 | ||||||||
| Funder: | National Research Foundation of Korea (NRF), Marie Skłodowska-Curie actions | ||||||||
| Grant number: | Project no. 2016S1A2A2912265 (NRF), Grant number 611693 |
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