Li, M.Z. (2020) Research of AGV Assignment Considering Charging Factors at Automated Container Terminals. Master Thesis, Dalian Maritime University.

has been cited by the following article:

• TITLE: AGV Scheduling Optimization of Automated Port Based on Disruption Management

  AUTHORS: Ching-Kuei Kao, Qi-Er Ke, King-Zoo Tang, Peng-Jung Lin

  KEYWORDS: Automated Port, Disruption Management, Automated Guided Vehicle Scheduling, Dijkstra Algorithm

  JOURNAL NAME: Journal of Transportation Technologies, Vol.14 No.3, July 23, 2024

  ABSTRACT: The recent rapid development of China’s foreign trade has led to the significant increase in waterway transportation and automated container ports. Automated terminals can significantly improve the loading and unloading efficiency of container terminals. These terminals can also increase the port’s transportation volume while ensuring the quality of cargo loading and unloading, which has become an inevitable trend in the future development of ports. However, the continuous growth of the port’s transportation volume has increased the horizontal transportation pressure on the automated terminal, and the problems of route conflicts and road locks faced by automated guided vehicles (AGV) have become increasingly prominent. Accordingly, this work takes Xiamen Yuanhai automated container terminal as an example. This work focuses on analyzing the interference problem of path conflict in its horizontal transportation AGV scheduling. Results show that path conflict, the most prominent interference factor, will cause AGV scheduling to be unable to execute the original plan. Consequently, the disruption management was used to establish a disturbance recovery model, and the Dijkstra algorithm for combining with time windows is adopted to plan a conflict-free path. Based on the comparison with the rescheduling method, the research obtains that the deviation of the transportation path and the deviation degree of the transportation path under the disruption management method are much lower than those of the rescheduling method. The transportation path deviation degree of the disruption management method is only 5.56%. Meanwhile, the deviation degree of the transportation path under the rescheduling method is 44.44%.