Kunihiro Hamada

The International Maritime Organization (IMO) is pursuing reductions in greenhouse gas (GHG) emissions from international maritime transport. This paper presents a comprehensive review of simulation studies on IMO’s GHG measures, summarizing their simulation models and key components used. Our analysis reveals that these studies frequently rely on diverse assumptions that are often insufficiently documented, making it challenging to compare simulation results across studies. To address this critical issue, we propose a harmonized framework for maritime simulation models based on the review and findings. This framework provides structured variables and normalized units across domains, including demand, port operations, vessel characteristics, and fuel types, which the reviewed articles often assumed. The proposed framework is validated by an application example showing comparable tables of assumptions adopted by two different simulation studies. We anticipate that this framework will enable researchers to clearly present structured assumptions in their manuscripts, thereby enhancing readers’ understanding of assumptions and facilitating more efficient comparisons of simulation results across the field.

Container ports are pressured for efficiency while reducing greenhouse gas (GHG) emissions under decarbonization rules. Simulation-based operational planning frameworks are increasingly being explored for container terminal decarbonization and coordination analysis. This study fills the gap by developing a trace-driven offline digital twin (DT) framework integrated with a discrete event simulation model decision support for tactical planning and emissions assessment at Tanjung Priok International Container Port. Historical terminal operating system data were used to reproduce real operational conditions and evaluate alternative operational strategies without disrupting live terminal operations. Using deterministic simulations, the effects of coordination strategies, such as yard allocation, equipment deployment, and truck–vessel synchronization, on crane productivity, turnaround time, equipment utilization, and emissions were analyzed. The strategies impacted productivity, truck turnaround time, equipment utilization, and lifecycle GHG emissions. Monte Carlo simulations, the uncertainty and variability of environmental outcomes under different operational conditions were analyzed. The results show that coordination-based strategies outperform simple expansion in reducing congestion and emissions. Truck–vessel synchronization strategies and workload redistribution across yard space and time provide substantial operational and environmental benefits. The proposed framework should therefore be interpreted as a trace-driven offline DT discrete event simulation environment for tactical scenario evaluation rather than a fully synchronized real-time digital twin.

Pipe routing in ship design presents significant challenges because of its time-consuming nature and the need for considerable attention to detail. In recent years, the decreasing number of pipe designers has impacted the quality of pipe-routing designs. However, optimizing pipe routing is crucial for reducing construction costs and ship production time. This study focused on optimizing the procedure and routes, considering over 500 pipes throughout the engine room of an 82,000-ton Panamax bulk carrier with three decks. Procedure optimization was based on a genetic algorithm and a system that considered individual pipe characteristics, such as type, diameter, and length. For pipe-route optimization, we used the Dijkstra algorithm, which aims to provide the shortest pipe routing by minimizing branching, bending, crossing, and obstacle avoidance. Pipe-routing optimization was divided into “basic” and “detailed” designs to derive rough and detailed routes, respectively. The basic design allowed intersections and horizontal bending, while the detailed design provided a route without intersections and minimized all bending. The optimized design was compared with pipe routing designed by diameter and shorter-path orders. In ship construction, pipe-routing optimization reduced the overall cost of pipe routing by 7% compared to other systems that typically use diameter and shortest-route orders. The study’s findings highlight the practical cost benefits and potential applications of the pipe-routing process in ship construction.

Sea transportation such as that by container ships has an essential role in the economy both locally and internationally. Ships are a major commodity in distributing goods over long distances due to their relatively low price compared to air shipping. The study implemented an optimization method using heuristic algorithms with ship route selection to minimize operational costs based on the parameters of mileage between 12 ports in the Asia-Pacific region. The ship speed, engine power, and fuel prices at each port are processed using asymmetric traveling salesman problem modeling (ATSP). The research uses three different algorithms to compare with the performance of the traveling salesman problem, namely the nearest neighbor algorithm, simulated annealing, and a genetic algorithm, with an objective function of keeping fuel costs that ships will incur to a minimum. The results show that the genetic algorithm provides the route with the lowest fuel cost.

Extensive floating macroalgae have drifted from the East China Sea to Japan’s offshore area, and field observation cannot sufficiently grasp their extensive spatial and temporal changes. High-spatial-resolution satellite data, which contain multiple spectral bands, have advanced remote sensing analysis. Several indexes for recognizing vegetation in satellite images, namely, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and floating algae index (FAI), are useful for detecting floating macroalgae. Thresholds are defined to separate macroalgae-containing image pixels from other pixels, and adaptive thresholding increases the reliability of image segmentation. This study proposes adaptive thresholding using Sentinel-2 satellite data with a 10 m spatial resolution. We compare the abilities of Otsu’s, exclusion, and standard deviation methods to define the floating macroalgae detection thresholds of NDVI, NDWI, and FAI images. This comparison determines the most advantageous method for the automatic detection of floating macroalgae. Finally, the spatial coverage of floating macroalgae and the reproducible combination needed for the automatic detection of floating macroalgae in Kagoshima, Japan, are examined.

Dynamic changes in the global market demand affect ship development. Correspondingly, big data have provided the ability to comprehend the current and future conditions in numerous sectors and understand the dynamic circumstances of the maritime industry. Therefore, we have developed a basic ship-planning support system utilizing big data in maritime logistics. Previous studies have used a ship allocation algorithm, which only considered the ship cost (COST) along limited target routes; by contrast, in this study, a basic ship-planning support system is reinforced with particularized COST attributes and greenhouse gas (GHG) features incorporated into a ship allocation algorithm related to the International Maritime Organization GHG reduction strategy. Additionally, this system is expanded to a worldwide shipping area. Thus, we optimize the operation-level ship allocation using the existing ships by considering the COST and GHG emissions. Finally, the ship specifications demanded worldwide are ascertained by inputting the new ships instance.

The establishment of maritime safety and security is an important concern. Ship position prediction for maritime situational awareness (MSA), as a critical aspect of maritime safety and security, requires a longer time interval than collision avoidance and maritime traffic monitoring. However, previous studies focused mainly on shorter time-interval predictions ranging from 30 min to 10 h. A longer time-interval ship position prediction is required not only for MSA, but also for efficient allocation of ships by shipping companies in accordance with global freight demand. This study used an end-to-end tracking method that inputs the previous position of a vessel to a trained deep learning model to predict its next position with an average 24-h interval. An AIS dataset with a long-time-interval distribution in a nine-year timespan for capesize bulk carriers worldwide was used. In the first experiment, a deep learning model of the Indian Ocean was examined. Subsequently, the model performance was compared for six different oceans and six primary maritime chokepoints to investigate the influence of each area. In the third experiment, a sample location within the Malacca Strait area was selected, and the number of ships was counted daily. The results indicate that the ship position can be predicted accurately with an average time interval of 24 h using deep learning systems with AIS data.

Greenhouse gas (GHG) emissions from the global shipping sector have been increasing due to global economic growth. The International Maritime Organization (IMO) has set a goal of halving GHG emissions from the global shipping sector by 2050 as compared with 2008 levels, and has responded by introducing several international regulations to reduce the GHG emissions of maritime transportation. The impact of GHG emissions’ regulation and measures to curb them have been evaluated in the IMO’s GHG studies. However, the long-term influence of these GHG emission measures has not yet been assessed. Additionally, the impact of various GHG reduction measures on the shipping and shipbuilding markets has not been considered; accordingly, there is room for improvement in the estimation of GHG emissions. Therefore, in this study, a model to consider GHG emission scenarios for the maritime transportation sector was developed using system dynamics and was integrated into a shipping and shipbuilding market model. The developed model was validated based on actual results and estimation results taken from a previous study. Subsequently, simulations were conducted, allowing us to evaluate the impact and effectiveness of GHG emission-curbing measures using the proposed model. Concretely, we conducted an evaluation of the effects of current and future measures, especially ship speed reduction, transition to liquid natural gas (LNG) fuel, promotion of energy efficiency design index (EEDI) regulation, and introduction of zero-emission ships, for GHG emission reduction. Additionally, we conducted an evaluation of the combination of current and future measures. The results showed that it is difficult to achieve the IMO goals for 2050 by combining only current measures and that the introduction of zero-emission ships is necessary to achieve the goals. Moreover, the limits of ship speed reduction were discussed quantitatively in relation to the maritime market aspect, and it was found that the feasible limit of ship speed reduction from a maritime market perspective was approximately 50%.