Abstract
The running path of automated guided vehicles (AGVs) in the automated terminal is affected by the storage location of containers and the running time caused by congestion, deadlock and other problems during the driving process is uncertain. In this paper, considering the different AGVs congestion conditions along the path, a symmetric triangular fuzzy number is used to describe the AGVs operation time distribution and a multi-objective scheduling optimization model is established to minimize the risk of quay cranes (QCs) delay and the shortest AGVs operation time. An improved genetic algorithm was designed to verify the effectiveness of the model and algorithm by comparing the results of the AGVs scheduling and container storage optimization model based on fixed congestion coefficient under different example sizes. The results show that considering the AGVs task allocation and container storage location allocation optimization scheme with uncertain running time can reduce the delay risk of QCs, reduce the maximum completion time and have important significance for improving the loading and unloading efficiency of the automated terminal.
Highlights
With the increasingly fierce competition of ports and the rapid development of artificial intelligence technology, the automatic terminal has become an important trend in port development at home and abroad
As the link connecting the front and rear yard of the terminal, the horizontal transportation link connects with the quay cranes (QCs) and the yard cranes (YCs) to make the automated container terminal a whole
The automated guided vehicles (AGVs) travel time can be reduced by changing the stacking position of import containers in yard and the waiting time can be reduced by increasing the number of AGVs with QCs, which is an effective method to improve the efficiency of automatic terminal loading and unloading
Summary
With the increasingly fierce competition of ports and the rapid development of artificial intelligence technology, the automatic terminal has become an important trend in port development at home and abroad. The AGVs travel time can be reduced by changing the stacking position of import containers in yard and the waiting time can be reduced by increasing the number of AGVs with QCs, which is an effective method to improve the efficiency of automatic terminal loading and unloading. How to determine the operation time of AGVs and reduce the risk of delayed arrival of AGVs at the junction of QCs and YCs is the difficulty in solving the optimization problem of large-scale AGVs fleet scheduling. By expanding the fleet size of AGVs, the risk of AGVs arriving late at the junction of QCs can be reduced, the operating efficiency of QCs and YCs can be improved and the operating efficiency of automated terminal can be indirectly improved
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