Weather impact on containership routing in closed seas: A chance-constraint optimization approach

•We model weather impact in routing containerships at closed seas.•The problem is formulated as a chance constraint version of the VRP.•A pre-process step converts the chance constraint formulation to a deterministic equivalent.•A steady state genetic algorithm, coupled with a heuristic approach is...

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Veröffentlicht in:Transportation research. Part C, Emerging technologies Emerging technologies, 2015-06, Vol.55, p.139-155
Hauptverfasser: Kepaptsoglou, Konstantinos, Fountas, Grigorios, Karlaftis, Matthew G.
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Sprache:eng
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Zusammenfassung:•We model weather impact in routing containerships at closed seas.•The problem is formulated as a chance constraint version of the VRP.•A pre-process step converts the chance constraint formulation to a deterministic equivalent.•A steady state genetic algorithm, coupled with a heuristic approach is used for solving the model. Weather conditions have a strong effect on the operation of vessels and unavoidably influence total time at sea and associated transportation costs. The velocity and direction of the wind in particular may considerably affect travel speed of vessels and therefore the reliability of scheduled maritime services. This paper considers weather effects in containership routing; a stochastic model is developed for determining optimal routes for a homogeneous fleet performing pick-ups and deliveries of containers between a hub and several spoke ports, while incorporating travel time uncertainties attributed to the weather. The problem is originally formulated as a chance-constrained variant of the vehicle routing problem with simultaneous pick-ups and deliveries and time constraints and solved using a genetic algorithm. The model is implemented to a network of island ports of the Aegean Sea. Results on the application of algorithm reveal that a small fleet is sufficient enough to serve network’s islands, under the influence of minor delays. A sensitivity analysis based on alternative scenarios in the problem’s parameters, leads to encouraging conclusions with respect to the efficiency and robustness of the algorithm.
ISSN:0968-090X
1879-2359
DOI:10.1016/j.trc.2015.01.027