Adaptive path planning for dry bulk carriers under sudden maritime disturbances: A network-based approach

Journal article, 2026

In response to the demand for ship route planning under complex and dynamic maritime disturbances, this study proposes a three-stage approach encompassing ship route network construction, restricted area detour strategy design, and multi-objective path optimization for dry bulk carriers under maritime disturbances. To enable realistic network representation, a Grid-Based Spatial Clustering (GBSC) algorithm, improved from the Density-Based Spatial Clustering of Applications with Noise (DBSCAN), is developed to extract key route nodes from Automatic Identification System (AIS) data, thereby constructing a weighted undirected graph. Based on this network, typical restricted zones, modeled as circular or polygonal areas, are incorporated into the planning framework, and two corresponding detour strategies, namely node substitution and tangent-based rerouting, are introduced to maintain navigational feasibility. Furthermore, AIS-derived maneuvering behavior is integrated to interpolate continuous trajectories and assign adaptive sailing speeds. To evaluate route performance, a dual-objective optimization model that minimizes sailing time and carbon emissions is formulated, and an adaptive genetic algorithm is employed to optimize speed profiles. Experimental results in Northeast Asia demonstrate that the proposed method consistently produces feasible and cost-effective routes under diverse disturbance scenarios. Moreover, sensitivity analysis of cost weightings confirms the robustness and flexibility of the optimization model. Overall, the proposed framework provides a modular and data-driven foundation for intelligent route planning and offers practical support for environmentally sustainable and operationally efficient maritime decision-making.