To decrease the risk of collisions and ensure safe navigation, an Automatic Identification System (AIS) was developed to broadcast real-time ship states information, such as ship position and sailing speed. Due to the real-time characteristic of AIS data, it, hence, has been widely used to construct a data-driven model for ship trajectory prediction, which can provide onboard decision support for navigators. However, data provided by AIS is sometimes partially missing or simply wrong. Poor quality of AIS data can degrade the performance of data-driven models and lead to large uncertainty in the predicted ship positions. Therefore, this paper proposes a hybrid model to reduce uncertainty by integrating the multi-output Gaussian process (MOGP) model predictions and historical trajectory information. Historical trajectories provide useful prior knowledge to calibrate the performance of MOGP model predictions. This model is built through three steps: 1) extracting historical ship trajectory information from the route that a ship is following; 2) predicting ship positions with a data-driven predictor built by an MOGP; and 3) obtaining a trajectory by knowledge fusion of historical information and predicted results. The experiment shows the proposed hybrid model outperforms other data-driven models and has small errors and uncertainty quantification of the ship position prediction. Especially for the 6-minute position prediction, its RMSE is over 100 meters smaller than other methods.

In this paper, a new meta-heuristic optimization algorithm called Grey Wolf Optimizer (GWO) is applied to offshore crane design. An offshore crane is a pedestal-mounted elevating and rotating lifting device used to transfer materials or personnel to or from marine vessels, barges and structures whereby the load can be moved horizontally in one or more directions and vertically. Designing and building offshore cranes is a very complex process. It depends on the configuration of a large set of design parameters and is characterized by increased workability and functionality for the owner and cost effectiveness in the total cost of ownership. In an attempt to reduce time and cost involved in the design process, this paper defines a best set of design parameters and uses GWO for the automatic configuration of this set of parameters in a manner that increases the maximum safe working load of the crane and reduces its total weight. Results are verified by a comparative study with other Evolutionary Algorithms (EAs). Results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics.

This paper proposes several schemes for optimal scheduling of power producers in a shipboard power system for a typical offshore supply vessel, having a multiple number of possibly varying capacity gensets. The proposed scheduling methods are illustrated using four alternative power system configurations, ranging from a few large gensets to many gensets of smaller ratings. Mixed integer linear programming is used to formulate the optimization problems. Three formulations are presented: one for minimizing online capacity without further objectives, one that includes a redundancy constraint for loosing a group of gensets (to account for a worst-case failure scenario), and one to also balance running hours and minimizing connect/disconnect of the gensets. These different objectives can be combined and weighted based on importance, with or without redundancy constraints. Simulations are carried out to demonstrate the properties of the three different scheduling methods. The first method, minimizing online capacity only, is also used to illustrate the differences between the four genset configurations. This shows, for instance, that using more small gensets ensures generally a lower online available power (the connected capacity matches better the prevailing load) and near optimal loading of each genset around 80% (assuming equal loadsharing), at the same time as resilience to genset failures is preserved.

Given the increasing complexity of maritime traffic, particularly with the growing integration of autonomous vessels alongside conventional vessels, the need for enhanced coordination and decision support systems has become critical to ensure safety and efficiency. This article presents a concept for a centralised decision support system for local maritime traffic and can be used in centres for remote operations or vessel traffic services. By formalising a non-zero-sum asymmetric polymatrix game, we achieve collision and grounding avoidance of non-cooperative agents. Time-varying and state-dependent payoff matrices are constructed using a reward framework that combines metrics for compliance with maritime traffic rules, global collision risk, grounding risk, and an operational efficiency metric for speed and route optimisation. A pure Nash equilibrium is reached in a polymatrix game, a fully connected graph of traffic participants on vertices with bimatrix games on the edges. Strategy probability distributions in infinite populations located on the graph’s vertices are updated with the replicator revision protocol. The equilibrium constitutes a control message consisting of advised speed and heading commands broadcast to all traffic participants. To validate the proposed idea, we have developed a scalable multi-agent system simulator and present results from a collision avoidance scenario with three vessels and 49 strategies available to each. Engaged vessels achieved safe and efficient passage, guided by the advised speed and heading changes.