Reducing air pollution and greenhouse gas emissions has become one of the primary tasks for the shipping industry over the past few years. Among alternative marine fuels, liquefied natural gas (LNG) is regarded as one of the most popular alternative marine fuels because it is one of the cleanest fossil marine fuels. Therefore, a practical way to implement green shipping is to deploy dual-fuel ships that can burn conventional fuel oil and LNG on various ship routes. However, a severe problem faced by dual-fuel ships is methane slip from the engines of ships. Therefore, this study formulates a nonlinear mixed-integer programming model for an integrated optimization problem of fleet deployment, ship refueling, and speed optimization for dual-fuel ships, with the consideration of fuel consumption of both main and auxiliary engines, ship carbon emissions, availability of LNG at different ports of call, and methane slip from the main engines of ships. Several linearization techniques are applied to transform the nonlinear model into a linear model that can be directly solved by off-the-shelf solvers. A large number of computational experiments are carried out to assess the model performance. The proposed linearized model can be solved quickly by Gurobi, namely shorter than 0.12 s, which implies the possibility of applying the proposed model to practical problems to help decision-makers of shipping liners make operational decisions. In addition, sensitivity analyses with essential parameters, such as the price difference between the conventional fuel oil and LNG, carbon tax, and methane slip amount, are conducted to investigate the influences of these factors on operational decisions to seek managerial insights. For example, even under the existing strictest carbon tax policy, shipping liners do not need to deploy more ships and slow steaming to reduce the total weekly cost.

Maritime transport plays an important role in global economic development but also inevitably faces increasing pressures from all sides, such as ship operating cost reduction and environmental protection. An ideal innovation to address these pressures is electric ships, which are more environmentally friendly than conventional manned fuel oil ships. The electric ship is in its early stages. To provide high-quality transportation services, the service network needs to be designed carefully. Therefore, this research simultaneously studies the location of charging stations, charging plans, route planning, ship scheduling, and ship deployment under service time requirements. The problem is formulated as a mixed-integer linear programming model with the objective of minimizing total cost comprised of charging cost, construction cost of charging stations, and fixed cost of ships. A case study using the data of the shipping network along the Yangtze River is conducted in order to evaluate the performance of the model. Valuable managerial insights are also derived from sensitivity analyses.

Maritime decarbonization and strict international regulations have forced liner companies to find new solutions for reducing fuel consumption and greenhouse gas emissions in recent years. Green technology is regarded as one of the most promising alternatives to achieve environmental benefits despite its high initial investment costs. Therefore, a scientific method is required to assess the possibility of green technology adoption for liner companies. This study formulates a mixed-integer nonlinear programming model to determine whether to retrofit their ship fleets with green technology and how to deploy ships while taking maritime decarbonization into account. To convert the nonlinear model into a linear model that can be solved directly by off-the-shelf solvers, several linearization techniques are applied in this study. Sensitivity analyses involving the influences of the initial investment cost, fuel consumption reduction rate of green technology, unit fuel cost, and fixed operating cost of a ship on operation decisions are conducted. Green technology may become more competitive when modern technology development makes it efficient and economical. As fuel and fixed operating costs increase, more ships retrofitted with green technology will be deployed on all shipping routes.

Port state control (PSC) plays an important role in enhancing maritime safety and protecting the marine environment. Since the inspection resources are limited and the inspection process is costly and time-consuming, a critical issue for port states to guarantee inspection efficiency is to accurately select ships with a high risk for inspection. To address this issue, this study proposes three prediction models to predict the ship deficiency number and a ship selection optimization model based on the prediction results to target the riskiest ships for inspection. In addition to a linear regression model for ship deficiency number prediction solved by the least squares method, we establish two prediction models with the pairwise-comparison target based semi-“smart predict then optimize” (semi-SPO) method. Specifically, a linear programming model and a support vector machine (SVM) model are built and both have a loss function to minimize the sum of predicted ranking errors of each pair of ships regarding their deficiency numbers. We use the Hong Kong port as a case study, which shows that the SVM model based on the semi-SPO approach performs best among the three models with the least computation time and best ship selection decisions.