An AIS Data‐Driven Multi‐Area Continuous Berth Allocation Method Using Pair Barracuda Swarm Optimisation for Green and Intelligent Ports

ABSTRACT To achieve green and intelligent port resource sharing while improving the operational efficiency of collaborative scheduling, this paper proposes an automatic identification system (AIS) data‐driven multi‐area continuous berth allocation method based on the pair barracuda swarm optimisation (PBSO) algorithm. First, a mixed‐integer nonlinear programming model is formulated to optimize total operational costs, including ship waiting time, delayed departure penalties, berth deviation, fuel and shore power consumption during port operations. Among them, the real‐time AIS data dynamically eliminate ship arrival time uncertainties through continuous vessel position tracking, while spatiotemporal constraints ensure non‐overlapping allocations across functionally independent berth zones. Second, a novel berth zones scheduling algorithm is developed by adapting the PBSO framework, which is inspired by the hierarchical social structure and cooperative foraging behavior of barracuda pairs. By integrating a time‐adaptive search strategy and an enhanced memory storage mechanism, the algorithm significantly improves robustness. Comprehensive validation using four ship arrival schedules (S‐10, S‐30, S‐50, S‐100) demonstrates the method's effectiveness. The PBSO approach achieves an approximate 20% improvement over the standard PSO benchmark for S‐100 cases, significantly outperforming the other baseline methods. Finally, case studies in multi‐area continuous harbours of Luzon Island, Philippines, confirm that under dynamic AIS data updates, the proposed method achieves total cost reduction of approximately 7.90% for berth allocation plans. The dual validation from both simulation and real‐world implementation not only proves the algorithm's superior performance but also highlights its practical engineering value in modern port operations.