Towards sustainable shipping: A learning-aided route-speed joint optimization considering energy efficiency and punctual arrival

As the cost-efficient backbone for global supply chains, maritime transportation facilitates the intensive exchange of remote-manufacturing-site products, resulting in an annual global maritime fuel consumption exceeding 200 million tons and greenhouse gas emissions accounting for 2.89 % of anthropogenic emissions. The joint optimization of ship route and speed, a broadly applicable strategy for energy conservation and emission reduction in advancing sustainable shipping, has garnered considerable attention from both academia and industry. The core challenge in this field involve determining optimal route–speed combinations for a given ship and voyage under complex weather conditions, aiming to maximize energy efficiency while ensuring punctual arrival. Traditional heuristic algorithms struggle to efficiently explore the solution space, particularly in modern maritime optimization problems characterized by multiple competing objectives and high-dimensional decision spaces. Recent research has demonstrated that intelligent learning networks can drive the optimization process by extracting and utilizing latent evolutionary knowledge. However, their generated solutions frequently violate established maritime operational practices when deployed without domain-specific constraint integration, rendering them impractical for real-world ship navigation. Consequently, the energy-saving potential of ship route–speed optimization remains largely limited. To overcome these technical barriers, we propose an improved learning network integrated with tailored constraint mechanisms specific to route–speed settings. Not only does the network learn positive evolutionary patterns to achieve high-efficiency, directed optimization of individuals, but it also maximizes the feasibility of sailing plans for transoceanic shipping. Additionally, a series of improvement strategies, designed to facilitate the generation of a comprehensive Pareto-optimal set, are introduced to form an improved learning-aided optimization framework. In real-world case-based analyses, our proposed framework demonstrates superior performance compared to other state-of-the-art algorithms, achieving an average reduction of 9 % in ship energy consumption. Executable codes is available at https://github.com/Ldiper/MCL-EA.