To address the problems of high empty-load rates,delayed responses,and over-reliance on manual experience in the scheduling of shunting vehicles at bulk dry bulk cargo ports,a multi-objective bi-level optimal scheduling model is constructed to improve operational efficiency,reduce operating costs,and realize intelligent scheduling.The upper level of the model optimizes revenue equilibrium,the timeliness of high-priority tasks,and vehicle waiting time by associating work teams with operation plans.The lower level implements vehicle assignment with the objectives of proximity dispatching,revenue maximization and allocation fairness,and adopts Fully Loaded Both Ways strategy to reorganize homogeneous operation plans into closed-loop routes to reduce empty running mileage.Numerical experiments are conducted using Python and Gurobi based on actual port operation data.The results indicate that when the plan revenue weight is set between 0.4 and 0.6,the system achieves a balance among revenue fairness,the completion rate of high-priority tasks,and equipment configuration rationality.When the fairness weight ranges from 0.1 to 0.3,the system maintains an operational efficiency above 85% and realizes reasonable load distribution.Compared with traditional single-plan scheduling,Fully Loaded Both Ways strategy reduces travel distance by 30%-44% in 29 instances.The proposed model provides an effective optimization approach for the intelligent and low-cost operation of port transfer operations.