Effective route and speed decision-making can significantly reduce vessel operating costs and emissions. However, existing optimization methods developed for conventional fuel-powered vessels are inadequate for application to methanol dual-fuel ships, which represent a new energy vessel type. To address this gap, this study investigates the operational characteristics of methanol dual-fuel liners and develops a mixed-integer nonlinear programming (MINLP) model aimed at minimizing operating costs. Furthermore, an improved genetic algorithm (GA) integrated with the Nonlinear Programming Branch-and-Bound (NLP-BB) method is proposed to solve the model. The case study results demonstrate that the proposed approach can reduce operating costs by more than 15% compared to conventional route and speed strategies while also effectively decreasing emissions of CO₂, NOx, SOx, PM, and CO. Additionally, comparative experiments reveal that the designed algorithm outperforms both the GA and the Linear Interactive and General Optimizer (LINGO) solver for identifying optimal route and speed solutions. This research provides critical insights into the operational dynamics of methanol dual-fuel vessels, demonstrating that traditional route and speed optimization strategies for conventional fuel vessels are not directly applicable. This study provides critical insights into the optimization of voyage decision-making for methanol dual-fuel vessels, demonstrating that traditional route and speed optimization strategies designed for conventional fuel vessels are not directly applicable. It further elucidates the impact of methanol fuel tank capacity on voyage planning, revealing that larger tank capacities offer greater operational flexibility and improved economic performance. These findings provide valuable guidance for shipping companies in strategically planning methanol dual-fuel operations, enhancing economic efficiency while reducing vessel emissions.

Berthing path planning for ships in the presence of ocean currents is a challenging task. Several relevant methods have been proposed, but they all assume a constant surge velocity for the whole path. This hypothesis makes it impossible to achieve the simultaneous optimization of speed and energy consumption. To deal with this issue, in this paper an optimal control path planning method that considers the characteristics of currents is proposed. Unlike traditional algorithms such as the A* algorithm and genetic algorithm, the proposed improved hp-pseudospectral (IHP) method does not require a constant thrust assumption. In addition, it can obtain the Pareto optimal path under various current environments and optimize the sailing parameters. Specifically, first a multi-objective optimal control path planning model considering the kinematic characteristics of autonomous ships is established. Second, the IHP method is proposed to solve it, which includes dividing the time interval, subdomain collocation, and transformation optimization. Last, the optimality and high efficiency of the proposed method are proved theoretically. Simulation experiments show that the proposed method can provide solutions to this problem where existing algorithms fail and it has strong robustness and high efficiency. Furthermore, the obtained path is superior to those obtained using the existing algorithms in various scenarios; the results show that the proposed method outperforms A* and genetic algorithms in terms of path smoothness, energy efficiency, and computational economy, achieving reductions in travel distance, sailing time, and energy consumption by up to 6.0%, 6.9%, and 4.0%, respectively, compared with A, and by 3.3%, 4.4%, and 1.8%, respectively, compared with the genetic algorithm.