Integrated optimization of berth allocation and green energy bunkering for vessels

Optimizing port operational efficiency is essential for sustainable maritime logistics. However, traditional methods that address berth allocation or bunkering scheduling separately often produce suboptimal results due to their interdependencies, leading to low port turnaround efficiency. To address this, this paper proposes an integrated problem that jointly considers berth allocation, bunkering mode selection, and bunkering scheduling. The problem is formulated as a mixed-integer programming (MIP) model to minimize system costs, including vessel delay, berth deviation, and bunkering vessel operating costs under practical constraints. Then, an enhanced adaptive large neighborhood search (ALNS) algorithm with specialized operators and perturbation mechanisms is developed to solve it. Computational experiments demonstrate that the proposed algorithm performs well, and the model significantly reduces total costs compared with the two-step solution approach, especially under elevated port operational loads. Sensitivity analyses reveal how the numbers of berths and bunkering vessels jointly influence overall performance, offering practical insights for improving operational coordination and promoting sustainable port management.