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

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.

Automated summary

Macro-marine debris is a global issue, and this paper proposes using vessels and optimal routing network to collect it. By utilizing advanced technology and modeling software, a bi-objective model is developed to minimize travel time and carbon emission. The proposed algorithm balances efficiency, emission reduction, and vessel speed to achieve low carbon emission and cost effectiveness.