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.

Maritime unmanned aerial vehicles (UAVs) equipped with inertial navigation systems (INS) are prone to error accumulation, which can lead to excessive positioning errors and hinder their ability to perform long distance missions. To address this issue, this study first constructs a directed graph network for a flight area based on start and end points as well as error correction points. A multi-objective route planning model is then developed for a UAV, aiming to minimize both the flight distance and the number of positioning corrections. Considering the UAV’s turning radius, a trajectory length calculation model based on 3D Dubins curves is designed. Subsequently, a forward labeling-based multi-objective path planning algorithm is proposed to develop an optimization model. Experimental results demonstrate that the proposed method can effectively constrain the UAV’s horizontal and vertical positioning errors within 2.5 m, while optimally balancing flight distance and positioning accuracy to ensure the successful execution of long-range maritime UAV missions. The comparative results demonstrate that, while satisfying the positioning error requirements, our proposed method achieves a reduction of over 1.5% in total flight distance for maritime UAVs compared to the NSGA-II algorithm.