Various industries are undergoing transformation given recently available pervasive sensors, low-cost and low-latency digital communication, and distributed control technologies. The objective of this paper is to support the introduction of Internet of things (IoT) technologies in the maritime industry. The maritime industry is analyzed as a system of systems to define performance criteria and functions to be modeled and analyzed through simulation. In this case, the simulation of a shipping system includes models of operation, cargo loading, fuel loading, and docking for maintenance. In the simulation, various kinds of IoT technologies are defined by several input parameters. By changing the parameters, the simulator evaluates the impact of those technologies quantitatively. As a case study, 11 IoT technologies are evaluated and compared. The result reveals several insights that weight of the ship is the most impactful for the profit, controlling damage of the ship’s hull by operation is the most important for safety, and improvement in efficiency at ports is the key to reducing delay time in operation. Moreover, this paper shows that the sensitivity analysis by changing the input parameters can support the decision making of how much investment will be effective in considering the technologies’ levels.

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%.

This study explored how the GHG fuel standard (GFS) regulation affects international shipping quantitatively by simulating future fleet transformation, fuel consumption, and well-to-wake GHG emissions based on an inputted GHG fuel intensity pathway and scenario of transportation demand, fuel availability and costs, and technology availability. The method comprisesthree steps: calculating the amount of scrapped and built ships, allocating fuel type and mix, and calculating fuel consumption. We simulated how the regulation changes fleet composition and the mix of fuel consumption. From the perspective of feasibility and alternative fuels’ availability, some meaningful insights to implement the GFS regulation were delivered.

This paper aims to identify the gaps on the path to achieving sustainable development of floating offshore wind in Japan. Japan has a strong desire for floating offshore wind development, motivated by energy security, climate change, and industry promotion. The key challenges are described with an emphasis on the unique environmental conditions of Japan, such as earthquakes and tropical cyclones. In addition, the absence of oil and gas development in Japan has led to social challenges, such as a lack of supply chain, infrastructure, and human resources in offshore wind. A review of state-of-the-art technologies is provided in each technology domain for four research domains: site selection and characterization, technology and engineering, project execution and operation, and industry and economic enabling. The gaps identified in the paper suggest the need for specific research topics, such as the assessment of unique environmental conditions; the design of robust and cost-effective structures considering fabrication, transportation, installation, and operation; and the creation of a data sharing strategy for efficient and rapid learning. In addition, many challenges show technology gaps across domains, indicating the need for interdisciplinary research collaboration and system integration of complex systems with digital engineering approaches. Finally, the need for the development of an industry roadmap to address these challenges is discussed.

This study proposes a method for determining decision-making rules on fleet composition that are effective from a perspective of profitability in the shipping industry, where there are several market uncertainties such as fuel prices, freight rates, exchange rate, transportation demand, world transportation capacity, and ship price. A simulator is used to calculate the revenue of the fleet composition based on the market scenario. Using this to optimize the buying and selling behavior of ships, decision-making rules regarding fleet composition can be determined. The rules are expressed as a vector, and a genetic algorithm (GA) is used for optimization. As a case study, the method was applied to container ships operating between Asia and Europe. The simulation revealed optimal decision-making rules. Moreover, a controlled experiment was conducted to validate the effectiveness of the rules. Participants were randomly divided into two groups: those with rules and those without rules, and they were asked to perform buying and selling behavior in a simulated environment. By comparing these groups’ profits, it is validated that the rules discovered by the method are useful because they encourage decision-making from a long-term perspective.