Over the past decade, autonomous surface vessels (ASVs) have increasingly operated in a range of challenging environments involving safety-critical scenarios. Their navigational capabilities rely on rich and reliable sensor data, enabling accurate localisation, situational awareness and environmental perception. This allows ASVs to perform motion planning, collision avoidance and navigational control tasks. To ensure maritime safety, faults affecting onboard navigational sensors must be diagnosed. This paper presents a model-based fault diagnosis scheme for ASVs affected by multiple sensor faults. Model-based methods utilise available sensors and dynamical models for residual generation. However, models describing the navigation may vary considerably for ASVs due to differences in vessel types, actuator configurations and sensor setups. To address this challenge, multiple residuals are synthesised using observer-based monitoring modules in the navigational sensors. Considering the impact of uncertainties, the residuals are designed to be bounded by adaptive thresholds proposed for each monitoring module. Fault isolation is then performed using a combinatorial decision logic, achieved by grouping the available sensors into multiple sensor sets and supported by model-based sensitivity analysis. Finally, the effectiveness of the proposed scheme is verified through simulation examples of two real-world vessels of different types with different sensor and actuator configurations, thereby illustrating its application.

Existing studies on multi-vessel formations rarely combine physically based models of ship–ship hydrodynamic interaction with online formation control, so that energy benefits are typically assessed offline or only approximated through artificial potentials. This paper addresses this gap by embedding a reduced-order, hydrodynamics-aware resistance model into a hierarchical formation control framework for multi vessel systems. A three degree of freedom interaction model is incorporated into the cost function, enabling the supervisory controller to adaptively optimize inter ship spacing and formation geometry in a speed dependent and hydrodynamics aware manner. The lower level MPC ensures accurate trajectory tracking and stability under the guidance of the top level optimization. Four simulation studies are conducted to evaluate the proposed method. The platooning formation is first analyzed as a reference, followed by the triangular formation, which achieves balanced tracking performance and stability. The echelon formation is then examined, demonstrating significant energy savings in medium to high speed regimes while maintaining yaw stability. Finally, an unconstrained optimization scenario is explored, where the system autonomously adapts its geometry without prescribed patterns, revealing emergent energy efficient and stable arrangements across different speed ranges. Results show that the proposed approach not only reduces resistance and improves energy efficiency but also enhances formation adaptability and robustness under varying operating conditions. These findings provide new insights into hydrodynamics aware cooperative control and the development of energy conscious fleet management strategies for future maritime transportation.

Offshore Wind Farms (OWF) are expected to play a crucial role in mitigating climate change and promoting sustainable development, nevertheless, Operation and Maintenance (O&M) costs can reach 25–30 % of the total cost. Efficient O&M strategies reduce maintenance frequency, downtime, and improve overall performance. This paper reviews O&M decision-making for fixed-bottom OWFs according to a three-level decision-making hierarchy, strategic, tactical, and operational, reflecting how decisions vary by scope and time horizon of reference. Strategic decisions are typically focused on the overall maintenance strategy for the wind farm. Tactical decisions are focused on the selection of the fleet and the management of spare parts. Operational decisions are focused on the scheduling of individual maintenance tasks on a daily or weekly basis and the routing of the vessels. Exploring how the different decision-making layers have been addressed in the literature leads to valuable insights into open challenges and paves the way for the development of new decision-making methods. In this paper we highlight the untapped potential of prognostic-driven scheduling, we identify the lack of comprehensive models and methods that encompass all decision-making echelons holistically, and we emphasize the need for the integration of environmental considerations into the decision-making process.

This paper introduces a model predictive control (MPC) strategy for solid oxide fuel cell (SOFC) systems, introducing thermal stress-aware power modulation. The proposed MPC approach incorporates a temperature rate-of-change constraint to manage local temporal and spatial temperature gradients in the SOFC during transient power modulation. The study evaluates the sensitivity and effectiveness of the temperature rate-of-change constraint under four different constraint parameter sets, spanning a range from fast to slow power modulation. A one-dimensional spatially discretised SOFC model is employed in the simulations to assess the resulting local temperature gradients. The results of this paper indicate that the proposed MPC strategy enhances transient power tracking performance compared to the conventional approach of using an electrical current rate-of-change constraint with 1%–17%, without a significant increase in the local temporal and spatial temperature gradients in the SOFC.

Ammonia is considered one of the most promising hydrogen and energy carriers for decarbonizing deep-sea shipping and other remote heavy-duty applications. The AmmoniaDrive power plant concept uniquely combines Solid-Oxide Fuel Cell (SOFC) and Internal Combustion Engine (ICE) technology to address the issue of how to convert e-ammonia, produced from renewable resources, into useful on-board power safely and effectively, without the need for fossil fuels as combustion promotor. This paper introduces the AmmoniaDrive concept, outlines the challenging combustion properties of ammonia and ammonia-hydrogen mixtures and provides a short review of Compression Ignition ICE research for ammonia-fuelled engines. Three promising combustion concepts are introduced to give direction to further numerical and experimental research.

Autonomous inland shipping offers a safer and more efficient form of transportation over water with the potential to reduce maritime carbon emissions. However, the operation of autonomous vessels presents unique challenges due to complex dynamics, varying traffic conditions, and environmental disturbances. To ensure the safe navigation of these vessels in confined inland waterways, it is crucial to address manoeuvring prediction and motion control challenges. Research focusing on these challenges disregards or only partially incorporates inland waterway characteristics related to the vessel and its surroundings. This study provides a comprehensive analysis of these key factors. By modelling the vessel using a modified Manoeuvring Modelling Group (MMG) model specifically tailored for confined waterways, hydrodynamic effects due to shallow water, channel banks, and current are accounted for. A nonlinear model predictive controller (NMPC) is employed for the vessel path following control under various scenarios, including straight channels, confluences, and river bends. It is observed that the hydrodynamic effects from the channel banks significantly impact vessel steering. Compared to conventional proportional-integral-derivative (PID) controllers, NMPC effectively reduces course deviations and cross-track errors under varying water depth and ship-to-bank distance conditions, while also requiring fewer rudder deflections. Furthermore, key performance metrics related to the control of inland waterway vessels are proposed to evaluate the controller's performance further. The NMPC control law demonstrates its effectiveness in capturing the hydrodynamic effects and improving navigation safety in confined waterways.

Waterborne transport is very important for moving freight and passengers globally. To make this transport more efficient, vessel design must adapt to changing missions, regulations and the occurrence of malfunctions. This paper presents the design of an intelligent decision-support framework to assist marine engineers and vessel operators in updating the system and control architecture of marine vessels before and during a mission. The connection between the system architecture and control design perspectives is enabled using a semantics-based technique. To this end, the multi-level vessel control system is described by a semantic database, a knowledge graph used to connect the components automatically, and quantitative service criteria. Considering the system architecture, the optimal modification is deduced using modularity and complexity criteria, originating from the field of network theory. On the control side, an intelligent automation supervisor is designed to make offline and online decisions regarding the energy deficit to execute a new mission and the active automation configuration during operation. For offline decisions, system architecture modifications are requested by the vessel designers to cover the energy deficit. During operation, switching between hardware and virtual sensors as well as switching between energy management controllers is implemented to handle the effects of sensor faults. The framework is successfully applied to a case study of a tugboat used to adapt to missions with different power requirements, while simulation results are used to indicate its application in supporting the decisions of vessel designers and human vessel operators.

This work presents a stochastic model predictive control approach to optimize the management of a meat supply chain with uncertain demand. The proposed approach considers the temperature-dependent deterioration of meat products and the multi-stage nature of the supply chain, including producers, warehouses, retailers, and customers. The management problem is formulated as a mixed-integer optimization problem, where the objective is to minimize the total cost of the supply chain while satisflying customer demand and quality requirements. The approach uses scenario-based optimization to account for different uncertainty sources. The results show that the proposed method effectively balances the conflicting objectives of minimizing costs and meeting demand and quality requirements while accounting for uncertainty.

Maritime Autonomous Surface Ships (MASS) are advancing the shipping industry, requiring a mixed waterborne transport system (MWTS) where human supervision provides a supporting role for maintaining safety and efficiency, particularly in complex scenarios. This study explores the dynamics of seafarers’ trust in MASS during collision avoidance (CA) scenarios involving a vessel approaching from the starboard side. An empirical study with 26 participants representing diverse maritime experience levels examined how time, demographic factors, and collision avoidance strategies influence trust. Using a linear mixed model (LMM), trust was found to fluctuate across navigation stages: gradual accumulation during the routine navigation stage, sharp dissipation during strategy determination and execution stages, and partial recovery at the final stage. Strategies aligned with maritime regulations and appropriately timed evasive actions fostered higher trust, while overly early or imminent actions reduced trust. Additionally, a factor analysis consolidated the five trust dimensions, including dependability, predictability, anthropomorphism, faith, and safety, into two aspects: System Competence, encompassing the first four dimensions, and Situational Safety, representing safety-related trust. Furthermore, Bayesian Network (BN) is developed to model trust in the autonomous decision-making of MASS, integrating human observers demographics and situational factors. The model captures sequential trust dependencies, revealing the cascading effects of trust across various stages and the role of System Competence in shaping overall trust in the entire decision-making process. These findings provide actionable insights for designing MASS that support trust-building and optimise collision avoidance strategies, contributing to safer and more efficient autonomous maritime operations.

This article focuses on the problem of collaborative collision avoidance (CCAS) for autonomous inland ships. Two solutions are provided to solve the problem in a distributed manner. We first present a distributed model predictive control (MPC) algorithm that allows ships to directly negotiate their intention to avoid collision in a synchronous communication framework. Moreover, we introduce a new approach to shape the ship’s behavior to follow the waterway traffic regulations. The conditional convergence toward a stationary solution of this algorithm is guaranteed by the theory of the alternating direction method of multipliers (ADMM). To overcome the problem of asynchronous communication between ships, we adopt a new asynchronous nonlinear ADMM (Async-NADMM) and present an asynchronous distributed MPC algorithm based on it. Several simulations and field experiments show that the proposed algorithms can guarantee a safe distance between ships in complex scenarios while following the traffic regulations. Furthermore, the asynchronous algorithm has an efficient computational time and satisfies the real-time computing requirements of ships in field experiments.

The inland waterway transport sector is facing increasingly stringent legislation to reduce emissions and improve energy efficiency. Speed planning has the potential to provide logistically compliant, energy-efficient, and emission-reducing voyages for inland vessels. However, current speed planning methods do not consider PM and NO_x emissions, nor do they consider alternative power systems to internal combustion engines (ICE) and full electric systems. These omissions have led to a lack of clarity on the impact of speed planning on the emission profile of inland vessels and the impact of alternative power systems on energy consumption. In this paper we propose a validated speed planning method that considers the emission profile (CO₂, PM₁₀, and NO_x) and different engine types for inland vessels in an leg-based speed planning approach while taking into account varying fairway water depth and speed. Through a use case we show that the vessel can achieve a 7.26% energy, 5.37% CO₂ and fuel, 3.85% NO_x, and 6.77% PM₁₀ reduction while maintaining the same arrival time; showing a distinct difference of this method compared to slow steaming. We also find that CO₂, NO_x, PM₁₀, and energy are not directly proportional when making speed adjustments. Finally, we analyze the adverse effects of emission control areas and emission limits on the energy consumption and arrival times of vessels with non-zero emissions propulsion.

Multiagent reinforcement learning (RL) training is usually difficult and time-consuming due to mutual interference among agents. Safety concerns make an already difficult training process even harder. This study proposes a safe adaptive policy transfer RL approach for multiagent cooperative control. Specifically, a pioneer and follower off-policy policy transfer learning (PFOPT) method is presented to help follower agents acquire knowledge and experience from a single well-trained pioneer agent. Notably, the designed approach can transfer both the policy representation and sample experience provided by the pioneer policy in the off-policy learning. More importantly, the proposed method can adaptively adjust the learning weight of prior experience and exploration according to the Wasserstein distance between the policy probability distributions of the pioneer and the follower. Case studies show that the distributed agents trained by the proposed method can complete a collaborative task and acquire the maximum rewards while minimizing the violation of constraints. Moreover, the proposed method can also achieve satisfactory performance in terms of learning speed and success rate.

Discrete manufacturing companies are challenged to transform their existing manufacturing system to be better prepared for the changes caused by unstable supply chains and new market regulations. Reconfigurable Manufacturing Systems is a manufacturing paradigm conceived to deal with change in a fast and cost-effective way. Most of the design methods for such recon-figurable systems do not take the information of existing manufacturing systems into account. Thus, this paper proposes a method to generate reconfigurable manufacturing alternatives from legacy factories' information. The proposed method uses the bill of materials and the initial production capacity as inputs. The potential use of the proposed method is demonstrated with an illustrative example.

The application of automated ground vehicles (AGVs) is well-established in closed environments such as port terminals, while their operation in open areas remains challenging. In this work, we set out to overcome this limitation by introducing platooning as a transfer mode in heterogeneous vehicle networks. We propose a collaborative transportation framework where different transportation companies use a shared platform for delivery tasks. To support decarbonization efforts in port hinterland transport, we consider fleets comprising electric AGVs (E-AGVs) and conventional trucks. These E-AGVs need to visit charging stations, modeled as battery swap stations (BSS), and join platoons to travel within the linking road segment. Each carrier has contracts with certain BSSs and shares these stations through the platform as part of the transportation plan. The platform functions as a demand and resource pooling mechanism, further offering platooning and infrastructure-sharing services. We model the interaction between the platform and carriers as a two-level constrained Stackelberg competition. An efficient solution algorithm, incorporating problem-specific heuristics and an adaptive large neighborhood search with dedicated destroy, repair, and intensification operators, is proposed. Extensive numerical experiments demonstrate the algorithm's performance on both existing and new benchmark instances. Our results highlight the platform's potential to streamline port-hinterland logistics, with E-AGV platoons significantly reducing costs and emissions.

This paper considers the presence of movable bridges in inland waterway transport, and presents a control framework for the joint dynamic coordination of bridge operations and autonomous vessel navigation to minimize waiting times of vessels at bridges. Simultaneous evolution of bridge occupancy and vessel position is captured by a control-oriented model that incorporates qualitative behavior in the form of propositional logic expressions. A model predictive control (MPC) strategy is designed considering adaptable bridge opening regimes to exploit vessel passage demand, and operational preferences of both vessel skippers and bridge operators are taken into account to reach fair trade-off decisions. A realistic case study pertaining to the Rhine-Alpine corridor is used to demonstrate the effectiveness of the approach. Appropriate key performance indicators (KPIs) are defined and employed for a quantitative comparison with a mixed-integer programming (MIP) strategy with fixed opening regimes. Furthermore, sensitivity to the main MPC parameters is examined by carrying out extensive testing to assess the effect of each design parameter on the solution.

Collision avoidance in maritime navigation, particularly between autonomous and conventional vessels, involves iterative and dynamic processes. Traditional path planning models often neglect the behaviours of surrounding vessels, while path predictive models tend to ignore ship interaction features essential in collision scenarios. This study proposes a decision-making framework for collision avoidance, particularly focused on the interaction between autonomous and conventional vessels. The framework integrates a human-preference-aware navigational trajectory prediction model into the collision avoidance process, enhancing the decision-making capabilities in dynamic and interactive environments. We first model human-controlled ship navigational preferences using a Long Short-Term Memory (LSTM) autoencoder combined with K-means clustering, by extracting key preferences from ship pairs identified through AIS data. These preferences, which reflect strategic trajectory adjustments in response to collision risks, are then incorporated into trajectory prediction using a Multi-Task Learning Sequence-to-Sequence (seq2seq) attention LSTM model. The predicted trajectories provide a basis for the decision-making framework, including a local path planner and a trajectory tracking controller, designed to dynamically adjust the predicted reference path for collision-free navigation and ensure its accurate tracking. The framework was validated using AIS data from the port of Rotterdam, identifying four distinct navigational preferences by combining an LSTM-autoencoder and clustering techniques and demonstrating improved prediction accuracy compared to other existing models. Simulation tests demonstrate that the framework utilises the predicted trajectories to inform decision-making, ensuring accurate path tracking while dynamically addressing collision risks for autonomous ships. By providing preference-aware and adaptive reference trajectories, the framework reduces the likelihood of MASS trajectory misinterpretation by conventional ships, thereby supporting proactive collision avoidance in mixed waterborne transport environments.

This study investigates the enhancement of Maritime Autonomous Surface Ships (MASS) navigation and path-planning through the integration of ontology-based knowledge maps (KM) with the Dynamic Window Approach (DWA), a fusion termed KM-DWA. The ontology-based KM model is important for MASS navigation, offering a framework for situational awareness, including contextual information fusion and decision-making evidence. This research enriches the KM model with collision avoidance rules from the International Regulations for Preventing Collisions at Sea (COLREGs), building upon our previous work on MASS's efficient and COLREGs-compliant navigation in encounter scenarios. The model provides navigational context, covers COLREGs rules and environmental factors, and recommends MASS actions for various scenarios as suggested by COLREGs. Moreover, an adapted DWA, tailored to maritime navigation, accounts for specific constraints and safety measures for MASS, utilising KM-derived situational awareness as constraints in its cost function for path planning. A significant innovation introduced here is a tiered safety distance model featuring proactive, defensive, and collision buffers to ensure rule-compliant and effective collision avoidance. This scheme enables MASS to take timely collision avoidance actions at both proactive and defensive distances, in line with COLREGs recommendations. The effectiveness of the KM-DWA algorithm is validated by comparing it with the basic DWA algorithm in single- and multi-vessel encounter scenarios. The experiment outcomes illustrate the integrated approach's superiority in terms of COLREGs compliance and collision avoidance rate, emphasising its ability to support COLREGs-compliant decision-making and enhance situational awareness in autonomous maritime operations.

Global synchromodal transportation is a promising strategy for providing efficient, reliable, flexible, and sustainable container shipping services across continents. It involves integrating multiple modes and routes owned by various operators to create a comprehensive transport plan. However, these operators often have their own local networks and are hesitant to cede control to a centralized platform. Instead, they prefer to share limited information in a coordinated manner to achieve a common goal without sacrificing their own benefits. This paper proposes a coordinated mechanism for global synchromodal transport planning, in which a global operator proposes incentives to local operators to select the most efficient modes and routes for shipping containers from one continent to another. An augmented Lagrangian relaxation approach is developed for the global operator to generate incentives, and a heuristic algorithm is designed to address the computational complexity of the optimization problems faced by local operators. We incorporate the proposed approaches with a rolling horizon framework to handle dynamic shipment requests received from spot markets and with a buffer strategy to address travel time uncertainties. The coordinated mechanism is tested on a real network between Asia and Europe, and results show that it can significantly increase total profits, reduce request rejections, and reduce infeasible transshipments compared to decentralized global transportation plans currently in use, particularly under scenarios with higher degrees of dynamism and uncertainty.

Offshore wind farms are a promising source of renewable energy, but they face significant challenges in terms of operation and maintenance (O&M). Traditional scheduling models often overlook the potential of condition-based maintenance (CBM). Addressing this gap, this paper introduces a novel framework, incorporating principles of Model Predictive Control (MPC), to optimize the O&M scheduling of offshore wind farms using prognostic-driven maintenance. The framework integrates probabilistic remaining useful life (RUL) prognosis in a mixed-integer linear programming (MILP) optimization model with a rolling horizon approach, in alignment with MPC’s predictive and adaptive decision-making approach. The optimization model determines the optimal time to replace each component by minimizing the expected cost over the expected lifetime. This approach seeks to achieve the lowest expense while guaranteeing the highest utilization rate of each component. For the case study presented, the total O&M costs are reduced by up to 15% with respect to corrective maintenance strategies.

Nowadays, many companies still conceive their logistic operations as a simple material replenishment of production plants and don’t invest money to structure their supply chain and make processes more efficient. In addition, the high complexity and the emerging uncertainties that are characterizing a more globalized, dynamic, and interconnected world stimulate businesses to innovate the management of their supplier network. Unexpected events, such as COVID-19 and the semiconductor crisis, have put companies in research for solutions that look to improve and strengthen the partnership with their suppliers. Digitization represents one of the most innovative and disruptive challenges in today’s supply chains. Indeed, the increasing amount of data retrievable from logistic and production processes today is yet not exploited enough in comparison with its potential benefits. Companies still work by silos and prefer to hide their information rather than sharing them with their partners. This research investigates the role of data visibility, in order to demonstrate its benefits in a complex supply chain. By collaborating with Ferrari on a Supplier Relationship Management (SRM) project, this paper presents the design of a supply chain control tower through Model Predictive Control. By simulating a Model Predictive Control (MPC) optimization model on a small part of Ferrari’s supplier network, the coordination, efficiency, and sustainability of the supply chain are assessed through a comparison with the current state and by evaluating the network’s performances in different logistic scenarios. Although this solution is presented as a decision-support tool, it is thought of as a key technology for the future development of autonomous supply chain operations.