Maritime Transport Research最新文献

{"title":"Creating a digital twin platform for maritime decarbonization by AI-assisted CII measure prediction: A case of chemical tanker","authors":"Hadi Taghavifar","doi":"10.1016/j.martra.2025.100141","DOIUrl":"10.1016/j.martra.2025.100141","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"9 ","pages":"Article 100141"},"PeriodicalIF":3.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the value of vessel information sharing","authors":"Pim Willem Antoon van Leeuwen,&nbsp;Rommert Dekker","doi":"10.1016/j.martra.2025.100140","DOIUrl":"10.1016/j.martra.2025.100140","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"9 ","pages":"Article 100140"},"PeriodicalIF":3.9,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145104523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maritime inventory routing with an application to fish feed distribution","authors":"Anders Bjelland ,&nbsp;Aksel Borgen ,&nbsp;Sjur Wold ,&nbsp;Kjetil Fagerholt ,&nbsp;Dimitri J. Papageorgiou ,&nbsp;Kristian Thun ,&nbsp;Simen Tung Vadseth","doi":"10.1016/j.martra.2025.100139","DOIUrl":"10.1016/j.martra.2025.100139","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"9 ","pages":"Article 100139"},"PeriodicalIF":3.9,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A composite port resilience index focused on climate-related hazards: Results from Greek ports’ living-labs","authors":"Amalia Polydoropoulou ,&nbsp;Adonis Velegrakis ,&nbsp;Georgios Papaioannou ,&nbsp;Ioannis Karakikes ,&nbsp;Efstathios Bouhouras ,&nbsp;Helen Thanopoulou ,&nbsp;Dimitrios Chatzistratis ,&nbsp;Isavela Monioudi ,&nbsp;Konstantinos Moschopoulos ,&nbsp;Antonis Chatzipavlis","doi":"10.1016/j.martra.2025.100136","DOIUrl":"10.1016/j.martra.2025.100136","url":null,"abstract":"<div><div>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</div></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"9 ","pages":"Article 100136"},"PeriodicalIF":3.9,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the barriers to autonomous shipping","authors":"Sarah Marie Malmquist,&nbsp;Ziaul Haque Munim","doi":"10.1016/j.martra.2025.100135","DOIUrl":"10.1016/j.martra.2025.100135","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"9 ","pages":"Article 100135"},"PeriodicalIF":3.9,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solving the container premarshalling problem with an auxiliary bay","authors":"Celia Jiménez-Piqueras ,&nbsp;Kevin Tierney","doi":"10.1016/j.martra.2025.100134","DOIUrl":"10.1016/j.martra.2025.100134","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"8 ","pages":"Article 100134"},"PeriodicalIF":3.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-accuracy prediction of vessels’ estimated time of arrival in seaports: A hybrid machine learning approach","authors":"Sunny Md. Saber ,&nbsp;Kya Zaw Thowai ,&nbsp;Muhammad Asifur Rahman ,&nbsp;Md. Mehedi Hassan ,&nbsp;A.B.M. Mainul Bari ,&nbsp;Asif Raihan","doi":"10.1016/j.martra.2025.100133","DOIUrl":"10.1016/j.martra.2025.100133","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"8 ","pages":"Article 100133"},"PeriodicalIF":3.9,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cost–benefit analysis and design optimization of wind propulsion systems for a Tanker retrofit case","authors":"M. Reche-Vilanova ,&nbsp;H.B. Bingham ,&nbsp;M. Fluck ,&nbsp;D. Morris ,&nbsp;H.N. Psaraftis","doi":"10.1016/j.martra.2025.100132","DOIUrl":"10.1016/j.martra.2025.100132","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"8 ","pages":"Article 100132"},"PeriodicalIF":3.9,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the destination port of fishing vessels utilizing transformers","authors":"Andreas Berntsen Løvland,&nbsp;Helge Fredriksen,&nbsp;John Markus Bjørndalen","doi":"10.1016/j.martra.2025.100131","DOIUrl":"10.1016/j.martra.2025.100131","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"8 ","pages":"Article 100131"},"PeriodicalIF":3.9,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring the impact of port congestion on containership freight rates","authors":"Nektarios A. Michail ,&nbsp;Konstantinos D. Melas","doi":"10.1016/j.martra.2025.100130","DOIUrl":"10.1016/j.martra.2025.100130","url":null,"abstract":"<div><div>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 &gt;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.</div></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"8 ","pages":"Article 100130"},"PeriodicalIF":3.9,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}