Research on the Cooperative Scheduling of ARMGs and AGVs in a Sea-Rail Automated Container Terminal under the Rail-in-Port Model

Reasonable scheduling of a train's loading and unloading equipment can reduce the energy consumption of production operations; this has great value for the green development of terminals. The collaborative scheduling model of the Automated Rail Mounted Gantry (ARMG) and Automated Guided Vehicle (AGV) is used to minimize the energy consumption of equipment in a scenario of a vertical railway entering a port and a shared storage yard existing between the port and railway under the mixed operation mode of train-ship and train-yard-ship. According to the characteristics of the model, the two-layer scheduling rule and the self-adaptive chaos genetic algorithm (SCGA) were proposed to solve the problem of placing the ARMG and the AGV on the same schedule. Simulation experiments verified the effectiveness of the model and algorithm. The effects of the delayed arrival of vessels, the proportion of transshipment containers, and the number of automated ARMGs and AGVs on total energy consumption were analyzed. The results showed that when all containers are train-ship containers, the number of ARMG and AGV at 1:4 will minimize the total operational energy consumption. Furthermore, as ships take longer to arrive, reducing the number of AGVs can cut energy use by 15% for the same number of ARMG.

Automated summary

Reasonable scheduling of loading and unloading equipment for trains can reduce energy consumption in production operations, which is crucial for environmentally-friendly development of terminals. A collaborative scheduling model for Automated Rail Mounted Gantry (ARMG) and Automated Guided Vehicle (AGV) was proposed to minimize equipment energy consumption in a scenario involving vertical railway entry to a port and a shared storage yard. The model employed a two-layer scheduling rule and a self-adaptive chaos genetic algorithm (SCGA) to optimize the placement of ARMG and AGV. Simulation experiments confirmed the effectiveness of the model and algorithm. The study also analyzed the effects of delayed vessel arrival, transshipment container proportion, and the number of automated ARMGs and AGVs on total energy consumption. The results indicate that a 1:4 ratio of ARMG to AGV minimizes energy consumption when all containers are train-ship containers. Furthermore, as ship arrival time increases, reducing the number of AGVs can significantly decrease energy use while maintaining the same number of ARMGs.