Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/14728
Title: A decision support system for vessel speed decision in maritimes logistics using weather archive big data
Authors: Aydin, N
Choi, Y
Lekhavat, S
Irani, Z
Keywords: speed optimization;sustainable maritime logistics;weather archive data;liner shipping;particle swarm optimization
Issue Date: 2017
Citation: Computers and Operations Research
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.
URI: http://bura.brunel.ac.uk/handle/2438/14728
ISSN: 0305-0548
Appears in Collections:Dept of Electronic and Computer Engineering Embargoed Research Papers

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