Method for allocation of loading and unloading equipment in container rail-water intermodal hub

To improve the efficiency of loading and unloading equipment allocation in rail-water intermodal transport container terminals and to reduce carbon emissions generated by container truck operations, a multi-objective combinatorial optimization model was developed considering the spatiotemporal characteristics of container flows and the rail-ship arrival under a hybrid handling mode of rail-water direct transfer and rail-yard-water operation. The optimization objectives were to minimize equipment operating costs, the turnaround times of trains and ships, and carbon emissions from container truck movements. A dynamic zone-based allocation strategy was proposed for container trucks. A discrete time-period equipment allocation mechanism was also introduced. The allocation numbers of quay cranes, rail-mounted gantry cranes, rubber tyred gantry cranes, and container trucks were dynamically determined. A genetic algorithm based on time-period allocation rules was designed to solve the model. The model was validated using 24-hour operational data from a rail-water intermodal transport container terminal. The results indicate that, in the original case, when the loading and unloading time windows of trains and ships overlap, the proportion of rail-ship direct transfer reaches 82%, the utilization rates of quay cranes, rail-mounted gantry cranes, and rubber tyred gantry cranes increase by 15%-20%, and the workload of rubber tyred gantry cranes in the export container yard and railway import container yard decreases by 15% and 23%, respectively. It proves that rail-ship direct transfer is the preferred transshipment mode for rail-water intermodal transport, which can significantly improve the utilization rate of terminal handling equipment, with significant impact of direct transfer capacity on the rubber tyred gantry cranes in railway import container yards. When the number of container trucks allocated to the direct-transfer zone reduces from 8 to 2-4 units, the total cost increases by 1.2%-2.8% and carbon emissions from container truck movements rise by approximately 16%, which indicates that both total cost and carbon emissions are highly sensitive to direct transfer capacity. Furthermore, under a large-scale handling scenario involving 5 ships and 4 trains, equipment operating costs account for 84.12% of the total cost, whereas this proportion increases to 87.8% when the handling scale is reduced to 1 ship and 1 train. This suggests that the proposed dynamic equipment allocation mechanism is more effective in coordinating equipment resources under larger-scale operational conditions. Compared with directly solving the model using the MATLAB integer linear programming solver, the proposed algorithm lowers the total operating cost of the terminal by 19.1%. Compared with the standard genetic algorithm, it shortens the average solution time by 33.5% and improves the average solution quality by 37.62%, which indicates its superior stability and solution accuracy. An efficiency-oriented and environmentally friendly decision-making approach can be provided for equipment configuration in rail-water intermodal transport container terminals. By dynamically allocating equipment quantities, the rail-ship direct transfer capability can be enhanced and yard congestion and storage pressure can be alleviated.