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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

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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
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:
DateEvent
October 2018Published
13 June 2017Available
4 June 2017Accepted
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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