Maritime Transport Research最新文献

Two-echelon synchronized routing and technician assignment for offshore wind farm maintenance 海上风电场维护的两梯队同步路由和技术人员分配

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2026-01-23 DOI: 10.1016/j.martra.2026.100149

Parisa Torabi, Ahmad Hemmati

Maintaining offshore wind farms (OWFs) is essential to ensure stable power production, but poses significant logistical and operational challenges due to the harsh marine environment, making it complicated to transport technicians and equipment.

This study introduces the problem of finding the route and assignment of technicians minimizing the total time required to complete all scheduled maintenance tasks at OWFs under three concurrent assumptions: (1) the compatibility of technicians’ skill-set and the maintenance task to be performed, (2) technicians’ routing in the form of drop-off and pick-up, and (3) synchronized two-echelon system, composed of accommodation vessel (AV) and crew transfer vessels (CTVs). Together, these assumptions create a realistic and operationally meaningful foundation for the problem. Skill-task compatibility ensures that only appropriately qualified technicians are assigned to each maintenance job, reflecting real workforce constraints. Modeling technicians’ movements as coordinated drop-off and pick-up routes cuts idle time of the vessels, as service times at turbines are typically much longer than the short travel times between them. Finally, the synchronized two-echelon system addresses the long commute distance from shore, enhancing operational efficiency.

The problem is modeled as a mixed integer linear program (MILP) model that accounts for various types of vessels and allows technicians to continue working independently after being dropped off. Finding feasible solutions to this problem is challenging, and solving it to optimality is extremely computationally complex. Thus, an adaptive matheuristic is designed to find high-quality solutions efficiently. Preliminary experiments on test instances based on Norwegian OWFs demonstrate that the proposed method yields robust and near-optimal solutions. We have also compared this method to a genetic algorithm that is adjusted to solve this specific problem and observe that, in most cases, the adaptive matheuristic reached better solutions with higher robustness. The impact of technician availability is also analyzed, showing that reducing crew size can significantly affect total operation time.

维护海上风电场（owf）对于确保稳定的电力生产至关重要，但由于恶劣的海洋环境，使得技术人员和设备的运输变得复杂，这给后勤和运营带来了重大挑战。本研究介绍了在三个假设条件下，如何找到技术人员的路线和分配问题，以最大限度地减少完成所有计划维修任务所需的总时间：(1)技术人员的技能组合与要执行的维修任务的兼容性；(2)技术人员的路线以下车和接送的形式出现；(3)由住宿船（AV）和船员转运船（CTVs）组成的同步双梯队系统。总之，这些假设为这个问题创造了一个现实的、具有操作意义的基础。技能任务兼容性确保只有适当合格的技术人员被分配到每个维护工作，反映了实际的劳动力限制。将技术人员的动作建模为协调的上下车路线，减少了船只的闲置时间，因为涡轮机的服务时间通常比它们之间的短暂旅行时间长得多。最后，同步的两梯队系统解决了距离海岸较长的通勤距离，提高了操作效率。该问题建模为混合整数线性规划（MILP）模型，该模型考虑了各种类型的船只，并允许技术人员在离开后继续独立工作。为这个问题找到可行的解决方案是具有挑战性的，并且将其求解到最优是非常复杂的计算。因此，设计了一种自适应数学算法，以有效地找到高质量的解。在基于挪威owf的测试实例上进行的初步实验表明，该方法具有鲁棒性和近似最优解。我们还将该方法与经过调整以解决该特定问题的遗传算法进行了比较，并观察到，在大多数情况下，自适应数学方法得到了更好的解，具有更高的鲁棒性。对技术人员可用性的影响也进行了分析，表明减少机组人员规模可以显著影响总作业时间。

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引用次数: 0

Spatio-temporal and operational clustering of maritime container terminal activities for scenario-based truck appointment planning 基于场景的卡车预约规划的海上集装箱码头活动时空和操作聚类

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2026-01-21 DOI: 10.1016/j.martra.2026.100148

Claudia Caballini , Julio Mar-Ortiz , Maria D. Gracia

In today’s uncertain and rapidly evolving global landscape, maritime container terminals are increasingly affected by operational disruptions such as yard congestion, unbalanced resources utilization and delays in container handling. Truck Appointment Systems (TAS) have emerged as a key strategy to regulate truck arrivals and smooth peak demand, yet their effectiveness remains limited by insufficient integration with yard-side dynamics. Appointment allocation is typically designed without accounting for the spatial and temporal variability of yard operations. Designing a robust TAS requires the definition of realistic and representative operational scenarios. This paper proposes an enhanced DBSCAN-based clustering framework designed to identify recurring operational scenarios in container terminals. This is performed by jointly analysing spatiotemporal truck arrival patterns and container handling behaviours. The algorithm extends traditional density-based clustering by incorporating multi-dimensional distance metrics that capture spatial proximity, temporal alignment and operational similarity between container movements. The application of the proposed approach to a real-world case study from an Italian container terminal demonstrates its ability to extract five recurrent operational scenarios, covering more than 90% of container movements, with a noise ratio of 9.7% and a High-Density Score (HDS) of 0.4253, indicating a good balance between cluster cohesion, coverage, and operational interpretability. The identified scenarios were further analysed and qualitatively validated through interactions with terminal planners and operational managers. Overall, the resulting clusters provide actionable insights to support robust TAS design, enabling the development of data-driven decision support tools that explicitly account for operational variability and enhance the resilience of terminal planning and management.

在当今不确定和快速变化的全球格局中，海上集装箱码头越来越多地受到堆场拥堵、资源利用不平衡和集装箱处理延迟等运营中断的影响。卡车预约系统（TAS）已成为调节卡车到达和平稳高峰需求的关键策略，但其有效性仍然受到庭院侧动态集成不足的限制。预约分配的设计通常没有考虑到堆场作业的空间和时间变化。设计健壮的TAS需要定义现实的、具有代表性的操作场景。本文提出了一个增强的基于dbscan的集群框架，用于识别集装箱码头中重复出现的操作场景。这是通过联合分析时空卡车到达模式和集装箱处理行为来执行的。该算法通过结合多维距离度量来扩展传统的基于密度的聚类，这些度量可以捕获容器移动之间的空间接近度、时间对齐和操作相似性。将该方法应用于意大利集装箱码头的实际案例研究表明，该方法能够提取五种重复的操作场景，覆盖90%以上的集装箱移动，噪声比为9.7%，高密度评分（HDS）为0.4253，表明集群内聚性、覆盖范围和操作可解释性之间的良好平衡。通过与码头规划人员和运营经理的互动，进一步分析和定性验证了确定的场景。总体而言，由此产生的集群提供了可操作的见解，以支持稳健的TAS设计，支持数据驱动的决策支持工具的开发，这些工具明确地考虑了操作的可变性，并增强了终端规划和管理的弹性。

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引用次数: 0

A decision-support model of icebreaker prepositioning for northern sea route navigation: A weighted-demand approach 北方航道破冰船预置决策支持模型：一种加权需求方法

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2026-01-06 DOI: 10.1016/j.martra.2025.100147

Chathumi Ayanthi Kavirathna , Ryuichi Shibasaki

The Northern Sea Route (NSR) has gained prominence as an international maritime corridor due to the retreat of Arctic sea ice, offering significant distance and time savings, particularly for shipping between Asia and Europe compared to conventional maritime routes. However, navigating the NSR involves considerable risks due to severe ice conditions along its navigation paths, making efficient icebreaking services critical for safe and reliable operations. The demand for icebreakers fluctuates spatially and temporally, depending on sea ice conditions and the ice class of cargo vessels. Additionally, icebreaking costs constitute a substantial portion of the overall voyage costs along the NSR. Strategically prepositioning icebreakers at optimum locations can help reduce these costs, enabling cargo vessels to request services more efficiently and minimize response times. This study focuses on the preparation stage of icebreaking services and introduces a weighted-demand response model to determine the optimum prepositioning of icebreakers before serving cargo vessels. The model considers expected vessel movements, navigation paths, and prevailing ice conditions. Eight Russian seaports are evaluated as potential prepositioning locations, and six ice classes—IC, IB, IA, IA-super, PC6, and PC5 are considered. The findings reveal that the optimum prepositioning locations and their priorities vary monthly in response to changing ice conditions, the composition of ice-class vessels, and their navigation directions. Moreover, Pevek Port consistently emerged as the highest-priority prepositioning location in most months. This study highlights the operational and policy implications of optimizing icebreaking services to reduce operating costs and improve the competitiveness of the NSR as an international maritime corridor.

由于北极海冰的消退，北海航线（NSR）作为国际海上走廊的地位日益突出，与传统海上航线相比，它提供了显著的距离和时间节省，特别是在亚洲和欧洲之间的航运。然而，在北极航道上航行存在相当大的风险，因为航道沿线的冰况严重，因此高效的破冰服务对安全可靠的作业至关重要。对破冰船的需求在空间和时间上都有波动，这取决于海冰状况和货船的冰级。此外，破冰船的费用在北极航道的总航行费用中占很大一部分。战略性地将破冰船预先部署在最佳位置可以帮助降低这些成本，使货船更有效地请求服务并最大限度地缩短响应时间。本文以破冰船服务的准备阶段为研究对象，引入加权需求响应模型来确定破冰船服务货船前的最佳预定位。该模型考虑了预期的船只运动、航行路径和当时的冰况。8个俄罗斯海港被评估为潜在的预先部署地点，并考虑了6个冰级- ic， IB， IA, IA-super， PC6和PC5。研究结果表明，最佳的预定位位置及其优先级随冰情、冰级船组成和航行方向的变化而变化。此外，在大多数月份，Pevek港一直是最优先的预先部署地点。本研究强调了优化破冰服务的运营和政策意义，以降低运营成本，提高西北航道作为国际海上走廊的竞争力。

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引用次数: 0

Learning to predict trajectories with destinations from massive vessel data 学习从大量船舶数据中预测目的地轨迹

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-12-19 DOI: 10.1016/j.martra.2025.100146

Jing Sun , Peng Wang , Fanjiang Xu , Zhaohui Liu

Accurate and real-time ship trajectory prediction is a premise for high-stake tasks such as risk reduction, route planning, energy saving, etc., and becomes more feasible based on the processing of AIS data with sophisticated algorithms, so as to ensure high-standard navigation by providing efficient trajectory-based maritime traffic management. In contrast to current prevailing research striving to improve short-term prediction accuracy, this paper focuses on whereabouts estimation in order to improve longer-term predictions for vessels. Taking the meaningful whereabouts as implicit destinations, the novel Destination-Guided Trajectory Prediction (DGTP) model is proposed, which employs a cascaded Seq2Seq architecture with BiGRU to simultaneously predict both vessel destination and trajectory. Trajectory Alignment Loss (TAL) is also introduced to encourage precise matching between the predicted and true trajectories in optimizing the DGTP model. Experiments conducted on a large volume of AIS data demonstrate that both destination prediction and TAL loss can independently improve trajectory prediction performances. Moreover, the synergistic combination of destination prediction and TAL within the DGTP model leads to substantial accuracy enhancements, demonstrating the promising results in long-term prediction.

准确、实时的船舶轨迹预测是实现降低风险、航线规划、节能等高风险任务的前提，通过对AIS数据进行复杂的算法处理，使其变得更加可行，从而通过提供高效的基于轨迹的海上交通管理，保证高标准的航行。与目前流行的研究努力提高短期预测精度相反，本文侧重于行踪估计，以提高船舶的长期预测。以有意义的位置作为隐式目的地，提出了一种新的目的地引导轨迹预测（destination - guided Trajectory Prediction， DGTP）模型，该模型采用级联的Seq2Seq架构和BiGRU同时预测船舶目的地和轨迹。在优化DGTP模型时，还引入了轨迹对准损失（TAL）来促进预测轨迹与真实轨迹之间的精确匹配。在大量AIS数据上进行的实验表明，目的地预测和TAL损失都可以独立提高轨迹预测性能。此外，DGTP模型中的目的地预测和TAL的协同组合导致了大量的精度提高，显示了长期预测的良好结果。

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引用次数: 0

Knowledge transfer Q-learning for vessel route planning using automatic identification system-derived expert trajectories 利用自动识别系统衍生的专家轨迹进行船舶航线规划的知识迁移q学习

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-12-01 DOI: 10.1016/j.martra.2025.100142

Hyunju Lee , Kikun Park , Hyerim Bae

Traditional route recommendation systems optimize navigation paths using environmental variables such as weather and sea conditions, but often fail to account for real-world factors encountered by mariners. To address this gap, this study proposes a knowledge transfer Q-learning (KT-QL) algorithm, a reinforcement learning method built upon the Q-learning framework. The proposed KT-QL algorithm integrates expert trajectory knowledge derived from Automatic Identification System data into the Q-learning process, enabling the agent to combine trial-and-error exploration with data-driven guidance. Experimental results show that KT-QL reduces Hausdorff distances by approximately 39 % compared with conventional reinforcement learning and traditional search methods, and enhances fuel consumption prediction accuracy by approximately 2 %. These findings highlight the potential of KT-QL to enhance maritime operational efficiency, safety, and environmental sustainability.

传统的路线推荐系统利用天气和海况等环境变量来优化导航路径，但往往无法考虑到海员遇到的现实世界因素。为了解决这一差距，本研究提出了一种知识转移q - ql算法，这是一种建立在q -学习框架之上的强化学习方法。提出的KT-QL算法将来自自动识别系统数据的专家轨迹知识集成到q -学习过程中，使智能体能够将试错探索与数据驱动引导相结合。实验结果表明，与传统强化学习和传统搜索方法相比，KT-QL将豪斯多夫距离降低了约39%，将油耗预测精度提高了约2%。这些发现突出了KT-QL在提高海上作业效率、安全性和环境可持续性方面的潜力。

引用次数: 0

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