Maritime Transport Research最新文献

英文中文

Creating a digital twin platform for maritime decarbonization by AI-assisted CII measure prediction: A case of chemical tanker 通过人工智能辅助CII测量预测创建海上脱碳数字孪生平台：以化学品船为例

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-10-10 DOI: 10.1016/j.martra.2025.100141

Hadi Taghavifar

Carbon emission reduction has been the focus of the International Maritime Organization (IMO), and restrictive mandates are considered by the Marine Environment Protection Committee (MEPC). The new guidelines consider carbon dioxide (CO₂) emissions based on the propulsion system efficiency, distance, and dead weight, which are called the carbon intensity indicator (CII). In this research, this factor was calculated based on the large available data from a chemical tanker ship to analyze the ship rating using artificial intelligence techniques. The available data, consisting of global positioning system (GPS) location, wind speed and direction, draft and trim, engine power and speed, and vessel speed, are used for the CII prediction by the artificial neural network (ANN) modeling. Two types of ANN are considered for modeling: multilayer feedforward with two hidden layers, called deep neural networks (DNN), and generalized regression neural networks (GRNN). The attained, required, and referenced CII are calculated, and the system rating is determined and compared with the predicted CII. The best performance of the DNN is achieved with 15 neurons in the first and second hidden layers. The performance of the two types of ANN is robust and close to each other. However, the GRNN has slightly better predictive efficiency, considering the faster convergence and setup configuration complexity. The GRNN model shows a mean absolute error of 0.0928 with an unacceptable prediction ratio of 0.06 % and a coefficient of determination R² = 0.998, which can capture the CII metric values and trend in transient mode robustly.

碳减排一直是国际海事组织（IMO）关注的焦点，海洋环境保护委员会（MEPC）也在考虑限制性指令。新指南考虑了基于推进系统效率、距离和自重的二氧化碳（CO2）排放量，这被称为碳强度指标（CII）。在本研究中，基于一艘化学油船的大量可用数据计算该因子，利用人工智能技术分析船舶额定值。利用全球定位系统（GPS）位置、风速和风向、吃水和纵倾、发动机功率和航速以及船舶航速等数据，通过人工神经网络（ANN）建模进行CII预测。两种类型的人工神经网络被考虑用于建模：具有两个隐藏层的多层前馈，称为深度神经网络（DNN）和广义回归神经网络（GRNN）。计算达到的、需要的和参考的CII，确定系统评级，并与预测的CII进行比较。深层神经网络的最佳性能是在第一层和第二层隐藏15个神经元。两类人工神经网络的性能都具有鲁棒性和接近性。然而，考虑到更快的收敛速度和设置配置复杂性，GRNN的预测效率略高。GRNN模型的平均绝对误差为0.0928，不可接受预测率为0.06%，决定系数R2 = 0.998，能较好地捕捉瞬态模式下的CII度量值和趋势。

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引用次数: 0

Assessing the value of vessel information sharing 评估船舶信息共享的价值

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-09-18 DOI: 10.1016/j.martra.2025.100140

Pim Willem Antoon van Leeuwen, Rommert Dekker

Efficient and timely vessel arrival planning is crucial for smooth operations in maritime transportation networks, ensuring optimal resource utilization and minimizing operational costs. When proforma schedules are disturbed by arrival deviations of vessels, waiting time and unnecessary fuel consumption become problems that shipping lines are faced with. Using a simulation model of a single-berth terminal, we test speed selection strategies for vessels that aim to minimize fuel, sailing, and waiting costs under varying availability of information. In different scenarios, we find optimality gaps ranging from 0.1% to 19.6% and show that knowing and communicating service end time to the vessel calling next could be valuable to integrated shipping lines and terminals.

高效、及时的船舶到达规划对于海上运输网络的顺利运行至关重要，可以确保资源的最佳利用和运营成本的最小化。当形式计划受到船舶到达偏差的干扰时，等待时间和不必要的燃料消耗成为航运公司面临的问题。利用单泊位码头的仿真模型，我们测试了在不同信息可用性下，以最小化燃料、航行和等待成本为目标的船舶的速度选择策略。在不同的情况下，我们发现最优性差距从0.1%到19.6%不等，并表明了解和沟通服务结束时间对下一个呼叫的船舶可能是有价值的。

引用次数: 0

Maritime inventory routing with an application to fish feed distribution 海上库存路由与应用的鱼饲料分配

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-08-14 DOI: 10.1016/j.martra.2025.100139

Anders Bjelland , Aksel Borgen , Sjur Wold , Kjetil Fagerholt , Dimitri J. Papageorgiou , Kristian Thun , Simen Tung Vadseth

This paper studies a maritime inventory routing problem (MIRP) faced by fish feed suppliers responsible for distributing different types of fish feed from one or several production facilities to a number of fish farms located at sea with a given heterogeneous fleet of specialized vessels. The feed supplier needs to maintain sufficient inventory levels at the farms at all times while minimizing the distribution costs. We propose a discrete-time mixed-integer programming (MIP) model for the fish feed MIRP. Since a commercial MIP-solver can only solve small problem instances, we also propose a matheuristic for solving real-life instances. The matheuristic employs a memetic algorithm, a metaheuristic combining a genetic algorithm with local search to decide how to route the vessels, coupled with a linear program for assigning quantities along the vessel routes. We perform a computational study on a number of realistic test instances generated using data from one of Norway’s largest fish feed suppliers. We show that the matheuristic produces reasonable solutions where the commercial MIP-solver fails, and as such can provide valuable decision support.

本文研究了鱼饲料供应商面临的海上库存路线问题（MIRP），这些供应商负责将不同类型的鱼饲料从一个或几个生产设施分发到位于海上的多个具有给定异构专用船队的养鱼场。饲料供应商需要在农场保持足够的库存水平，同时尽量减少配送成本。提出了一种鱼饲料MIRP的离散时间混合整数规划模型。由于商业mip求解器只能解决小问题实例，我们还提出了一个数学方法来解决现实生活中的实例。数学算法采用模因算法，一种结合遗传算法和局部搜索的元启发式算法来决定船只的路线，再加上沿船只路线分配数量的线性程序。我们对使用挪威最大的鱼饲料供应商之一的数据生成的一些实际测试实例进行了计算研究。我们表明，数学产生合理的解决方案，而商业mip求解器失败，因此可以提供有价值的决策支持。

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引用次数: 0

A composite port resilience index focused on climate-related hazards: Results from Greek ports’ living-labs 关注气候相关危害的综合港口恢复指数：来自希腊港口生活实验室的结果

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-06-23 DOI: 10.1016/j.martra.2025.100136

Amalia Polydoropoulou , Adonis Velegrakis , Georgios Papaioannou , Ioannis Karakikes , Efstathios Bouhouras , Helen Thanopoulou , Dimitrios Chatzistratis , Isavela Monioudi , Konstantinos Moschopoulos , Antonis Chatzipavlis

This paper develops a composite Port Resilience Index (PRI) to address the specific vulnerabilities and operational challenges of Greek ports in respect to climate-related hazards. Based on stakeholder engagement from Living Labs in three key ports (Chios, Volos, and Heraklion), the study identifies and quantifies the impacts of climate-related hazards using a structured Multi-Criteria Decision Analysis (MCDA) framework. Specifically, the Analytic Hierarchy Process (AHP) is used to elicit expert judgments and prioritize resilience criteria across five impact areas: Infrastructure, Operational and Supply Chain, Digital, Socioeconomic and Environmental, and Governance and Compliance Resilience. Nineteen indicators, spanning physical infrastructure, operational reliability, digital readiness, and socioeconomic factors, are evaluated to construct a composite PRI, enabling a transparent and stakeholder-informed benchmarking process. The results reveal significant variation in resilience levels, with Volos exhibiting the highest PRI (0.643) and Chios the lowest (0.217), thereby highlighting port-specific adaptation needs. Conducting a sensitivity analysis we validated the robustness of the PRI construction methodology across various weighting scenarios. The key contributions of this study are: (i) the development of a replicable, data-driven PRI model; (ii) the integration of local stakeholder input via Living Labs; and (iii) the innovative application of AHP to climate resilience planning in the port industry. Moreover, while focused on Greek ports, the framework offers a replicable model that can be adapted to other regions facing similar climate challenges. Ultimately, the PRI serves as both a diagnostic and strategic tool to guide policy, investment, and disaster preparedness in ports

本文开发了一个综合港口恢复指数（PRI），以解决希腊港口在气候相关危害方面的具体脆弱性和运营挑战。基于Living Labs在三个关键港口（希俄斯、沃罗斯和伊拉克利翁）的利益相关者参与，该研究使用结构化的多标准决策分析（MCDA）框架确定并量化了气候相关危害的影响。具体来说，层次分析法（AHP）用于在五个影响领域中得出专家判断并优先考虑弹性标准：基础设施、运营和供应链、数字、社会经济和环境以及治理和合规弹性。对19项指标进行评估，涵盖物理基础设施、运营可靠性、数字化准备和社会经济因素，以构建复合PRI，实现透明和利益相关者知情的基准流程。结果显示，各港口的适应能力水平存在显著差异，Volos的PRI最高（0.643），Chios最低（0.217），从而突出了港口的适应需求。通过敏感性分析，我们验证了PRI构建方法在各种加权方案中的稳健性。本研究的主要贡献是：(i)建立了一个可复制的、数据驱动的PRI模型；（ii）通过Living Labs整合当地利益相关者的意见；(3) AHP在港口行业气候适应性规划中的创新应用。此外，虽然该框架的重点是希腊港口，但它提供了一个可复制的模式，可以适用于面临类似气候挑战的其他地区。最终，PRI作为一种诊断和战略工具，指导港口的政策、投资和备灾

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引用次数: 0

Exploring the barriers to autonomous shipping 探索自主航运的障碍

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-06-21 DOI: 10.1016/j.martra.2025.100135

Sarah Marie Malmquist, Ziaul Haque Munim

The adoption of Maritime Autonomous Surface Ships (MASS) in commercial shipping presents significant challenges despite rapid technological advancements. This study explores the barriers to the commercial adoption of MASS. Through a systematic literature review, 60 barriers were identified and categorized into four themes: (1) human factors, (2) data and risk management, (3) technology and connectivity, and (4) operations and policy. To reveal the most critical barriers, the importance-improvement (A-B) analysis was conducted utilizing data collected from maritime stakeholders. The analysis revealed that the most critical barriers include the trustworthiness of autonomous technology, managing loss of autonomous control system, vulnerabilities to cyberattacks, and the complexities of regulatory compliance in system development and deployment. Future resources and investments should be directed towards addressing the most critical barriers identified in this study for ensuring the successful integration of MASS in commercial shipping.

尽管技术进步迅速，但在商业航运中采用海上自主水面舰艇（MASS）仍面临重大挑战。本研究探讨了MASS商业应用的障碍。通过系统的文献综述，确定了60个障碍，并将其分为四个主题：(1)人为因素，(2)数据和风险管理，(3)技术和连通性，(4)操作和政策。为了揭示最关键的障碍，利用从海事利益相关者收集的数据进行了重要性改进（A-B）分析。分析显示，最关键的障碍包括自主技术的可信度、管理自主控制系统的损失、网络攻击的脆弱性，以及系统开发和部署中法规遵从性的复杂性。未来的资源和投资应用于解决本研究确定的最关键的障碍，以确保MASS成功地纳入商业航运。

引用次数: 0

Solving the container premarshalling problem with an auxiliary bay 用辅助舱解决集装箱预编组问题

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-06-01 DOI: 10.1016/j.martra.2025.100134

Celia Jiménez-Piqueras , Kevin Tierney

The relocation of containers is essential at port terminals to increase operational efficiency during container retrieval from the yard. When a container must be retrieved, any container placed on top of it must be moved to another stack, delaying the retrieval process. The container premarshalling problem (CPMP) aims to tackle this issue by finding a sequence of minimal container relocations to achieve a bay arrangement where no container needs to be moved during retrieval. The classical formulation of this problem assumes that all premarshalling relocations occur within the bay being arranged. However, this study demonstrates that practical applications of premarshalling can benefit from more efficient use of available resources. We introduce a novel problem variant that allows the use of an auxiliary bay as additional space for relocating containers during the arrangement process. We present constraint programming solution methods for this variant that reveal a significant reduction in premarshalling relocations when an auxiliary bay is used. The results demonstrate that bays where high occupancy rates prevent premarshalling can be successfully arranged with an auxiliary bay. Additionally, we propose two alternative formulations allowing different rates of relocations between bays, offering adaptability to varying port terminal requirements.

集装箱的重新安置在港口码头是必不可少的，以提高从堆场取回集装箱的操作效率。当必须检索容器时，必须将放置在其顶部的任何容器移动到另一个堆栈，从而延迟检索过程。容器预编组问题（CPMP）旨在通过找到一系列最小的容器重新定位来解决这个问题，以实现在检索期间不需要移动容器的海湾安排。该问题的经典公式假设所有的预编组重新定位都发生在被安排的海湾内。然而，这项研究表明，预编组的实际应用可以从更有效地利用可用资源中受益。我们引入了一个新的问题变体，允许使用辅助舱作为在安排过程中重新安置集装箱的额外空间。我们提出了这种变体的约束规划解决方法，揭示了当使用辅助舱时，预编组重定位的显着减少。结果表明，在高入住率不利于预编组的情况下，可以成功地利用辅助海湾进行预编组。此外，我们提出了两种可供选择的配方，允许在海湾之间进行不同的重新定位，以适应不同的港口码头要求。

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引用次数: 0

High-accuracy prediction of vessels’ estimated time of arrival in seaports: A hybrid machine learning approach 船舶到达海港时间的高精度预测：一种混合机器学习方法

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-04-26 DOI: 10.1016/j.martra.2025.100133

Sunny Md. Saber , Kya Zaw Thowai , Muhammad Asifur Rahman , Md. Mehedi Hassan , A.B.M. Mainul Bari , Asif Raihan

Optimizing the Estimated Time of Arrival (ETA) for seaport-bound vessels is crucial to maritime operations since inaccurate ETA predictions can have a ripple effect, causing vessel schedule disruptions, congestion, and decreased port operational effectiveness. To address these challenges and fill substantial deficiencies in existing prediction models, we have introduced a novel hybrid tree-based stacking machine learning framework integrating Extra Trees, AutoGluon Tabular, and LightGBM, with Random Forest Regressor (RFR) as the meta-learner. Utilizing Automatic Identification System (AIS) data from vessels in the Baltic Sea, our model significantly improves ETA predictions, achieving a mean absolute percentage error (MAPE) of 0.25 %. Compared to existing machine learning algorithms, our stacking model exhibits superior prediction performance. Our study's feature importance analysis highlights the crucial role of variables like speed, distance, course, and vessel type in ETA forecasts. Cross-validation further confirms the robustness of our ensemble model. In conclusion, this study improves predictive analytics in marine logistics by giving useful information about real-time ETA estimates. This helps port authorities make the best use of their resources, reduces vessel idle time and congestion, and increases overall efficiency and sustainability. This way, this study can significantly contribute towards attaining operational excellence and provide a strong foundation for future predictive models, advancing smart port management and maritime logistics.

优化海港船舶的预计到达时间（ETA）对海上运营至关重要，因为不准确的ETA预测可能会产生连锁反应，导致船舶时间表中断、拥堵和港口运营效率下降。为了解决这些挑战并填补现有预测模型的实质性不足，我们引入了一种新的混合基于树的堆叠机器学习框架，该框架集成了Extra Trees、AutoGluon Tabular和LightGBM，并以随机森林回归器（RFR）作为元学习器。利用波罗的海船只的自动识别系统（AIS）数据，我们的模型显着提高了ETA预测，实现了0.25%的平均绝对百分比误差（MAPE）。与现有的机器学习算法相比，我们的叠加模型具有更好的预测性能。我们研究的特征重要性分析强调了速度、距离、航线和船舶类型等变量在ETA预测中的关键作用。交叉验证进一步证实了我们的集成模型的稳健性。总之，本研究通过提供有关实时ETA估计的有用信息，改进了海洋物流的预测分析。这有助于港口当局充分利用其资源，减少船舶闲置时间和拥堵，并提高整体效率和可持续性。通过这种方式，本研究可以为实现卓越运营做出重大贡献，并为未来的预测模型、推进智能港口管理和海上物流提供坚实的基础。

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引用次数: 0

Cost–benefit analysis and design optimization of wind propulsion systems for a Tanker retrofit case 某油轮改造案例中风力推进系统的成本效益分析与设计优化

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-04-19 DOI: 10.1016/j.martra.2025.100132

M. Reche-Vilanova , H.B. Bingham , M. Fluck , D. Morris , H.N. Psaraftis

This study introduces WindWise, a cost–benefit analysis and design optimization tool for Wind Propulsion Systems (WPS) in sustainable shipping. By integrating route simulations, ship constraints, and fuel pricing scenarios, WindWise determines the optimal WPS configuration to maximize fuel savings and minimize payback periods. A retrofit case study of an oil tanker evaluates two WPS classes—DynaRigs and Rotor Sails—across multiple operational and economic conditions. Results reveal that optimal configurations vary based on constraints: in an unconstrained scenario, larger, well-spaced installations minimize aerodynamic losses, whereas realistic constraints shift the preference towards smaller, distributed setups to mitigate cargo loss and air draft penalties. Rotor Sails offer lower upfront costs and shorter payback periods for modest savings targets and for side-wind routes, while DynaRigs emerge as the more viable solution for higher emissions reductions and long-term profitability. Optimization of WPS configurations proves crucial, with non-optimized configurations exhibiting payback periods over 150% higher than optimized ones. Although payback period remains an important metric, considering both payback and net present value provides a more comprehensive assessment of WPS financial viability, with Rotor Sails generally offering faster payback but DynaRigs delivering higher long-term profitability across most scenarios.

本研究介绍了风力推进系统（WPS）在可持续航运中的成本效益分析和设计优化工具 WindWise。通过整合航线模拟、船舶约束条件和燃料定价方案，WindWise 可以确定最佳的 WPS 配置，以最大限度地节省燃料并缩短投资回收期。通过对一艘油轮的改造案例研究，评估了两类 WPS--DynaRigs 和 Rotor Sails--在多种运营和经济条件下的性能。研究结果表明，最佳配置会因限制条件的不同而有所变化：在无限制条件的情况下，较大、间距合理的装置可最大限度地减少空气动力损失，而在现实限制条件下，则更倾向于较小、分布式的装置，以减少货物损失和吃水损失。旋翼风帆的前期成本较低，投资回收期较短，可实现适度的节能目标和侧风航线，而 DynaRigs 则是更可行的解决方案，可实现更高的减排量和长期盈利能力。事实证明，优化 WPS 配置至关重要，非优化配置的投资回收期比优化配置高出 150% 以上。尽管投资回收期仍是一个重要指标，但同时考虑投资回收期和净现值可以更全面地评估 WPS 的财务可行性，一般来说，旋翼风帆的投资回收期更快，但在大多数情况下，DynaRigs 的长期盈利能力更高。

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引用次数: 0

Predicting the destination port of fishing vessels utilizing transformers 利用变压器预测渔船目的港

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-03-17 DOI: 10.1016/j.martra.2025.100131

Andreas Berntsen Løvland, Helge Fredriksen, John Markus Bjørndalen

Vast databases on historical ship traffic are currently freely available in the form of AIS (Automatic Identification System) messages dating back to as early as 2002. This provides a rich source for training deep learning models for predicting various behaviors of vessels, which in this context is motivated by resource management of fisheries. In this paper, we explore the possibility for combining a transformer model’s powerful capabilities for long-term path prediction with added logic to infer probable destination harbors for fishing vessels. An additional baseline model is also developed for comparison, based on historically preferred harbors for the vessels. With AIS data from the Troms and Finnmark region of Norway, the prediction accuracy of the trained model is found to be highly dependent on the number of past tracked positions of the vessel. We foresee that a new and more precise model will need to incorporate not only dynamic AIS data, but static information about harbors and vessel types during training and inference.

关于历史船舶交通的庞大数据库目前以AIS（自动识别系统）信息的形式免费提供，最早可追溯到2002年。这为训练用于预测船舶各种行为的深度学习模型提供了丰富的资源，在这种情况下，这些模型是由渔业资源管理驱动的。在本文中，我们探索了将变压器模型强大的长期路径预测能力与附加逻辑相结合的可能性，以推断渔船可能的目的地港口。还根据历史上船舶的首选港口开发了一个额外的基线模型进行比较。使用来自挪威Troms和Finnmark地区的AIS数据，发现训练模型的预测精度高度依赖于船只过去跟踪位置的数量。我们预计，在训练和推理过程中，一个新的、更精确的模型不仅需要包含动态AIS数据，还需要包含有关港口和船舶类型的静态信息。

引用次数: 0

Measuring the impact of port congestion on containership freight rates 衡量港口拥堵对集装箱船运价的影响

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-01-21 DOI: 10.1016/j.martra.2025.100130

Nektarios A. Michail , Konstantinos D. Melas

We examine the impact of port congestion on containership freight rates. Our overall results show that port congestion has a positive and significant effect on containership freight rates. The most important region is Asia, where a 1 % increase in port congestion has a >1 % effect on shipping freight rates. This suggests that the region, being the world's largest manufacturing area and an integral part of the supply chain, has much more importance than previously considered. As such, the results highlight the importance of the manufacturing region in supply chains and are also in line with the derived demand system in shipping. As per the results, a return to the pre-pandemic congestion levels in Asia would lead to at least a 25 % decline in containership freight costs.

我们研究港口拥挤对集装箱船运价的影响。我们的总体结果表明，港口拥堵对集装箱船运价有显著的正向影响。最重要的地区是亚洲，在那里，港口拥堵每增加1%，对航运运费的影响就会增加1%。这表明，作为世界上最大的制造业地区和供应链不可分割的一部分，该地区的重要性远比之前认为的要大得多。因此，结果突出了制造业区域在供应链中的重要性，并且也符合航运的衍生需求系统。根据研究结果，如果亚洲恢复到大流行前的拥堵水平，集装箱船货运成本将至少下降25%。

引用次数: 0

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