Highlights A bi-objective model of vessel routing and discrete speed is proposed. A novel hybrid metaheuristic algorithm is proposed. Environmentally friendly solutions are also cost-effective. No Pareto solution contains a combination of high and low speeds. Speed has a bigger impact than a route scheme in Pareto solution set.

    Abstract Macro-marine debris becomes a global thorny issue to human beings and people are keeping trying every means to mitigate the risk of it. In this paper, we propose to use vessel and develop optimal vessel routing network to collect macro-marine debris on the nearshore surface. Due to the integrated force of winds and ocean currents, debris constantly changes floating location in the ocean. Advanced remote sensing technology and General National Oceanic and Atmospheric Administration (NOAA) Operational Modeling Environment (GNOME) software are employed to identify debris locations and track the drifting trajectory, respectively. In order to balance collecting efficiency and vessel emission reduction, we propose a bi-objective mixed integer nonlinear programming model for vessel routing to minimize travel time and carbon emission, considering time window at debris location, vessel capacity, low/medium/high vessel speed, and cost including carbon tax. A novel pheromone heuristic adaptive large neighborhood search (PHALNS ) algorithm combined with archived multi-objective simulated annealing (AMOSA) mechanism is developed to solve the proposed model. The Yangtze River Estuary region is taken as a numerical example to verify the proposed model and algorithm. Six criterions are introduced to evaluate the best collection time, and the proposed algorithm is also compared with NSGA II algorithm. The results show that low carbon emission and cost effective could be achieved simultaneously, and vessel speed has larger impact than the route scheme.


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    Title :

    Floating marine debris mitigation by vessel routing modeling and optimization considering carbon emission and travel time


    Contributors:
    Duan, Gang (author) / Fan, Tao (author) / Chen, Li (author) / Ma, Junfeng (author)


    Publication date :

    2021-10-22




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






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