Roll-on roll-off (RoRo) shipping plays an important role in freight transport on the European continent, and is faced with the challenge of reducing its CO2 emissions while increasing its efficiency. Dual cycling, in which loading and discharging processes are carried out simultaneously, achieves this goal by reducing the turnaround time of vessels in port and thus the CO2 output of handling equipment in port and fuel consumption through slow steaming at sea. Optimizing the dual cycling operations on RoRo vessels has not yet been investigated in the literature. This paper presents the novel RoRo dual cycling problem (RRDCP), and formulates it using integer programming (IP) with the objective to minimize the total makespan of discharging and loading operations. We further prove that the RRDCP is NP-complete by a reduction from a general machine scheduling problem, and introduce a novel heuristic to solve the problem called a generalized random key algorithm (GRKA). We evaluate the IP model and GRKA approach on both generated and industrial instances, showing that the GRKA heuristic finds optimal or near-optimal solutions to real-world problems in just seconds. We provide managerial insights on industrial instances, which indicate that our approach leads to a reduction in fuel consumption and CO2 emissions of up to 25% for RoRo operations.
This paper proposes a multi-time scale scheduling strategy for a practical port coupled hydrogen-electricity energy system (CHEES) to optimize the integration of renewable energy and manage the stochasticity of port power demand. An optimization framework based on day-ahead, intra-day and real-time scheduling is designed. The framework allows coordinating adjustable resources with different rates to reduce the impact of forecast errors and system disturbances, thus improving the flexibility and reliability of the system. The effectiveness of the proposed strategy is verified by a case study of the actual CHEES in the Ningbo Zhoushan Port, and the impact of equipment anomalies on the port power system operation is studied through simulation of different scenarios. The results show that compared with a scheduling scheme without energy management strategy, CHEES with multi-time scale scheduling can save 25.42% of costs and reduce 14.78% of CO 2 emissions. A sensitivity analysis is performed to highlight the impact of hydrogen price and soft open points (SOP) rated power on the system economy. This study not only provides a new perspective for the optimal scheduling of port energy systems, but also provides a practical framework for managing port energy systems to achieve green transformation and sustainable development.
Ship designers face increasing pressure to comply with global emission reduction ambitions. Alternative fuels, potentially derived from bio-feedstock or renewable electricity, provide promising solutions to this problem. The main challenge is to identify a suitable ship power system, given not only uncertain emission requirements but also uncertain fuel and carbon emission prices. We develop a two-stage stochastic optimization model that explicitly considers uncertain fuel and carbon emission prices, as well as potential retrofits along the lifetime. The bi-objective setup of the model shows how the choice of optimal power system changes with reduced emission levels. Methanol and LNG configurations appear to be relatively robust initial choices due to their ability to run on fuel derived from different feedstocks, and their better retrofittability towards ammonia or hydrogen. From a policy perspective, our model provides insight into the effect of the different types of carbon pricing mechanisms on a shipowner's decisions.
Implementation of alternative energy supply solutions requires the broad involvement of local communities. Hence, smart energy solutions are primarily investigated on a local scale, resulting in integrated community energy systems (ICESs). Within this framework, the distributed generation can be optimally utilised, matching it with the local load via storage and demand response techniques. In this study, the boat demand flexibility in the Ballen marina on Samsø—a medium-sized Danish island—is analysed for improving the local grid operation. For this purpose, suitable electricity tariffs for the marina and sailors are developed based on the conducted demand analysis. The optimal scheduling of boats and battery energy storage system (BESS) is proposed, utilising mixed-integer linear programming. The marina’s grid-flexible operation is studied for three representative weeks—peak tourist season, late summer, and late autumn period—with the combinations of high/low load and photovoltaic (PV) generation. Several benefits of boat demand response have been identified, including cost savings for both the marina and sailors, along with a substantial increase in load factor. Furthermore, the proposed algorithm increases battery utilisation during summer, improving the marina’s cost efficiency. The cooperation of boat flexibility and BESS leads to improved grid operation of the marina, with profits for both involved parties. In the future, the marina’s demand flexibility could become an essential element of the local energy system, considering the possible increase in renewable generation capacity—in the form of PV units, wind turbines or wave energy
Seaborne trade is the lynchpin in almost every international supply chain, and about 90% of non-bulk cargo worldwide is transported by container. In this survey we give an overview of data-driven optimization problems in liner shipping. Research in liner shipping is motivated by a need for handling still more complex decision problems, based on big data sets and going across several organizational entities. Moreover, liner shipping optimization problems are pushing the limits of optimization methods, creating a new breeding ground for advanced modelling and solution methods. Starting from liner shipping network design, we consider the problem of container routing and speed optimization. Next, we consider empty container repositioning and stowage planning as well as disruption management. In addition, the problem of bunker purchasing is considered in depth. In each section we give a clear problem description, bring an overview of the existing literature, and go in depth with a specific model that somehow is essential for the problem. We conclude the survey by giving an introduction to the public benchmark instances LINER-LIB. Finally, we discuss future challenges and give directions for further research.
The number of marine scrubbers installed in industry has been on the rise over the past decade and is expected to continue in the coming years. Therefore, it is essential to ensure that the design of the scrubbers enables as an efficient operation as possible. In this study, an optimization of the exhaust cover inside an in-line scrubber was carried out. The optimization was done by combining a computational fluid dynamics model working on a simplified geometry with the method of feasible directions in order to reduce the pressure loss caused by the exhaust cover. The design is constrained in both height and width of the points making up the exhaust cover to ensure proper drainage of water and to avoid invalid designs. It was found that the optimized design reduced the pressure loss by 42% compared to the initial design. Furthermore, the scalability of the original design was investigated with the same height constraint enforced on the design variables. The result of the scalability analysis showed that the radius of the exhaust cover for the optimal designs scales linearly with the diameter of the scrubber, while the pressure loss was found to increase quadratically as the diameter of the scrubber increases.
Major liner shipping companies offer pre- and end-haulage as part of a door-to-door service, but unfortunately pre- and end-haulage is frequently one of the major bottlenecks in efficient liner shipping due to the lack of coordination between customers. In this paper, we apply techniques from vehicle routing problems to schedule pre- and end-haulage of containers, and perform tests on data from a major liner shipping company. The paper considers several versions of the scheduling problem such as having multiple empty container depots, and having to balance the empty container depot levels. The influence of the side constraints on the overall cost is analysed. By exploring the fact that the number of possible routes in the considered case is quite limited, we show that the model can be solved within a minute by use of column enumeration. Alternative constraints and problem formulations, such as balancing empty container storage level at depots, are considered. Computational results are reported on real-life data from a major liner shipping company.
Efficient control schemes of Autonomous underwater vehicle (AUV) are challenging due to uncertainties and highly nonlinearities. In this paper, improved fractional order PID controller is proposed for the control of AUV motion with six degrees of freedom (DOF). Genetic algorithm and Particle Swarm Optimization (PSO) are employed to find suboptimal coefficients of FOPID controller to improve performance of the AUV motion. These optimal adjusted coefficients of FOPID controllers minimize the step response characteristics such as maximum deviation and settling time. Simulation results are presented to verify the advantages of the FOPID with respect to the previous works specially proportional-integral-derivative controller (PID).
Driven by increased integration of renewable energy sources, the widespread decarbonization of power systems has led to energy price fluctuations that require greater adaptability and flexibility from grid users in order to maximize profits. Industrial loads equipped with flexible resources can optimize energy consumption rather than merely reacting to immediate events, thereby capitalizing on volatile energy prices. However, the absence of sufficient measured data in industrial processes limits the ability to fully harness this flexibility. To address this challenge, we present a black-box optimization model for optimizing the energy consumption of cooling systems in the aquaculture industry using Extreme Gradient Boosting (XGBoost) and Bayesian Optimization (BO). XGBoost is employed to establish a nonlinear relationship between cooling system power consumption and available measured data. Based on this model, Bayesian Optimization with the Lower Confidence Bound (LCB) acquisition function is used to determine the optimal discharge temperature of water into breeding pools, minimizing day-ahead electricity costs. The proposed approach is validated using real-world data from a case study at the Port of Hirtshals, Denmark based on measurements from 2023. Our findings illustrate that leveraging the inherent flexibility of industrial processes can yield financial benefits while providing valuable signals for grid operators to adjust consumption behaviors through appropriate price mechanisms. Furthermore, machine learning techniques prove effective in optimizing energy consumption for industries with limited measured data, delivering accurate and practical estimates.
The design of emission control areas (ECAs), including ECA width and sulfur limits, plays a central role in reducing sulfur emissions from shipping. To promote sustainable shipping, we investigate an ECA design problem that considers the response of liner shipping companies to ECA designs. We propose a mathematical programming model from the regulator’s perspective to optimize the ECA width and sulfur limit, with the aim of minimizing the total sulfur emissions. Embedded within this regulator’s model, we develop an internal model from the shipping liner’s perspective to determine the detoured voyage, sailing speed, and cargo transport volume with the aim of maximizing the liner’s profit. Then, we develop a tailored hybrid algorithm to solve the proposed models based on the variable neighborhood search meta-heuristic and a proposition. We validate the effectiveness of the proposed methodology through extensive numerical experiments and conduct sensitivity analyses to investigate the effect of important ECA design parameters on the final performance. The proposed methodology is then extended to incorporate heterogeneous settings for sulfur limits, which can help regulators to improve ECA design in the future.