Sea State Decadal Variability in the North Atlantic: A Review

Long-term changes of wind-generated ocean waves have important consequences for marine engineering, coastal management, ship routing, and marine spatial planning. It is well-known that the multi-annual variability of wave parameters in the North Atlantic is tightly linked to natural fluctuations of the atmospheric circulation, such as the North Atlantic Oscillation. However, anthropogenic climate change is also expected to influence sea states over the long-term through the modification of atmospheric and ocean circulation and melting of sea ice. Due to the relatively short duration of historical sea state observations and the significant multi-decadal variability in the sea state signal, disentangling the anthropogenic signal from the natural variability is a challenging task. In this article, the literature on inter-annual to multi-decadal variability of sea states in the North Atlantic is reviewed using data from both observations and model reanalysis.

Near-Field Route Optimization-Supported Polar Ice Navigation via Maritime Radar Videos

The accurate design of ship routing plans in arctic areas is not easy, considering that navigation conditions (e.g., weather, visibility, and ice thickness) may change frequently. A ship’s crew identifies sea ice in arctic channels with the help of radar echoes, and ship maneuvering decisions are made to avoid navigation interference. Ship officials must manually and consistently change the ship’s route of travel, which is time-consuming and tedious. To address this issue, we propose a near-field route optimization model for the purpose of automatically selecting an optimal route with the help of radar echo images. The ship near-field route optimization model uses a multiobjective optimal strategy considering factors of minimum navigation risk and steaming distance. We verified the model’s performance with the support of the Xuelong voyage dataset. This research finding can help a ship’s crew to design more reasonable navigation routes in polar channels.

Unsupervised Reconstruction of Sea Surface Currents from AIS Maritime Traffic Data Using Trainable Variational Models

The estimation of ocean dynamics is a key challenge for applications ranging from climate modeling to ship routing. State-of-the-art methods relying on satellite-derived altimetry data can hardly resolve spatial scales below ∼100 km. In this work we investigate the relevance of AIS data streams as a new mean for the estimation of the surface current velocities. Using a physics-informed observation model, we propose to solve the associated the ill-posed inverse problem using a trainable variational formulation. The latter exploits variational auto-encoders coupled with neural ODE to represent sea surface dynamics. We report numerical experiments on a real AIS dataset off South Africa in a highly dynamical ocean region. They support the relevance of the proposed learning-based AIS-driven approach to significantly improve the reconstruction of sea surface currents compared with state-of-the-art methods, including altimetry-based ones.

Stochastic Programming for Liner Ship Routing and Scheduling under Uncertain Sea Ice Conditions

It is anticipated that in the foreseeable future the Northern Sea Route (NSR) will be able to serve commercial shipping as an alternative transportation shortcut between East Asia and Europe, especially in the summer season. The sailing time, however, is heavily subject to the variation of sea ice conditions along this route. Any participating shipping company must consider how to mitigate the ill effects on itinerary planning caused by sailing time and cost uncertainty. Finding a good trade-off between the benefit from a tight schedule and the risk caused by an unexpected delay is a key element in relevant routing and scheduling decisions, and may be beyond the reach of traditional deterministic planning models. With the aim of maximizing profit over all possible shipping environment scenarios, this article proposes a two-stage stochastic nonlinear integer programming model for liner ship routing and scheduling with uncertain shipping time and cost, the nonlinearity of which arises from the coexistence of schedule-sensitive shipping demand and uncertain arrival time variables in the objective function. The model is converted into an equivalent linear integer programming counterpart by introducing a set of nominal delay variables, and Benders decomposition is applied to solve the linearized problem. Numerical experiments and sensitivity analyses are conducted to validate the efficacy and effectiveness of the model, the results of which suggest several managerial insights that can be used to guide liner ship route and schedule planning under uncertain shipping conditions.

A Ship Routing System Applied to the Statistical Analysis of the Western Mediterranean Wave Trends

The Western Mediterranean basin is a busy route by Short Sea Shipping with an important route between Barcelona (Spain) and Genoa (Italy), where climatic patterns show similarities but vary during the year. One essential topic for Short Sea Shipping competitiveness is the time because distances use to be covered in approximately 24–30 h. To optimize the transit time, meteorological variables must be kept in mind. In this contribution, we compare data collected by buoys and data simulated using the SIMROUTE (ship weather routing software), to draw a map of wave tendencies during the year. The resulting map of the investigation shows the wave height in percent. The result can be used to optimize the existing routes between Barcelona and Genoa improving his competitiveness and safety.

A Set Covering Model for a Green Ship Routing and Scheduling Problem with Berth Time-Window Constraints for Use in the Bulk Cargo Industry

This paper presents a set covering model based on route representation to solve the green ship routing and scheduling problem (GSRSP) with berth time-window constraints for multiple bulk ports. A bi-objective set covering model is constructed with features based on the minimization of the total CO2 equivalent emissions and the total travel time subject to a limited number of berths in each port, berthing time windows, and the time window for each job. The solutions are obtained using the ε-constraint method, after which a Pareto frontier is plotted. This problem is motivated by the operations of feeder barges and terminals, where the logistics control tower is used to coordinate the routing and berthing time of its barges. We show that the proposed method outperforms the weighted sum method in terms of the number of Pareto solutions and the value of the hypervolume indicator.

Autonomous and Optimized Ship Routing

We present state-of-the-art computational methods which are instrumental in autonomous maritime operations, and optimization of routing, scheduling as well as loading. Our aim is to survey mature algorithmic approaches developed within the Lab of Geometric and Algebraic Algorithms, towards exploiting intelligence and automation in modern shipping and, in particular, in various aspects of routing. We showcase our advances in two main axes: (a) geometric computing for collision avoidance in complex environments, thus allowing for semi-autonomous and fully autonomous navigation, and (b) optimization for routing under time constraints of the carrier ship, time windows of availability at the ports of call, and capacity constraints of various compartments within a vessel.