Abstract
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.
Highlights
Speed optimization in liner shipping 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 (UNCTAD, 2010; Psaraftis and Kontovas, 2013)
Sailing speed decision mainly depends on the vessel schedule and it is a challenging problem due to the uncertainties imposed in maritime logistics such as stochastic port times and weather conditions
We focus on the speed optimization problem by considering the effect of weather conditions on fuel consumption
Summary
Speed optimization in liner shipping 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 (UNCTAD, 2010; Psaraftis and Kontovas, 2013). Weather archive data provides an opportunity to apply big data analytics to estimate the degree of the impacts of weather conditions on fuel consumption of vessels in different routes based on its huge volume of historical data. Most of such archive data is not easy to use due to the format, volume, and velocity of data. This paper proposes a decision support system (DSS) that uses weather archive big data in vessel speed optimization overcoming above challenges.
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