Double-Cycling AGV Scheduling Considering Uncertain Crane Operational Time at Container Terminals

Container terminals (CTs) play an important role in the modern logistics and transportation industry. The utilization of automated guided vehicles (AGVs) can be effectively facilitated by reducing their empty running. The existing strategies cannot guarantee the full load of AGVs during their transportation because of the complex constraints of container scheduling. This work proposes a double-cycling AGV scheduling model that ensures a full load of AGVs between the quayside and the yard. The objective is to minimize the total waiting time of AGVs and ensure a high loading rate of AGVs by scheduling loading/unloading containers. Furthermore, it takes the randomness of the quay crane's operational time into consideration. By assigning a time interval to AGVs' arrival at a quayside, a container scheduling sequence is obtained based on a Hybrid Particle Swarm Optimization (HPSO) algorithm with a penalty function. Via experiments, it shows that the proposed model can obtain the least number of AGVs for container transportation, minimize AGVs' total waiting time, and ensure the high loading rate of AGVs.

Automated summary

This study proposes a double-cycling AGV scheduling model to minimize the total waiting time of AGVs and ensure a high loading rate. By considering the randomness of quay crane's operational time, a container scheduling sequence is obtained through a hybrid particle swarm optimization algorithm.