New requirements for terminal production and operation have emerged as a result of the increase in container terminal throughput. Traditional terminals’ manufacturing capabilities fall short of the expanding service needs. By constructing a digital twin yard for container terminals, the production capacity of terminals can be effectively improved, and the production operation process can be optimized. This paper firstly constructs a digital twin yard system for container terminals, proposing that it is mainly composed of physical space, virtual space, data, services, and intelligent agents. This paper elaborates on the core technologies of digital twin yards and finally takes the container delivery and loading process as an example to solve the production bottlenecks of the yard in the container delivery business by reorganizing the operation process and targeting it, which can improve the terminal production efficiency to a certain extent.

Xia, M.; Li, Y.; Shen, Y., and Zhao, N., 2020. Loading sequencing problem in container terminal with Deep Q-Learning. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 817–821. Coconut Creek (Florida), ISSN 0749-0208.Loading sequencing is a scheduling problem in container terminal to reduce handling cost and improve handling efficiency of loading process by rearranging the loading sequencing preplanned by stowage planning. Loading sequencing orchestrates terminal equipment to handle export container loading efficiently. To obtain good solving efficiency in large scale scheduling cases, a double Deep Q-Network with prioritized experience replay is proposed to solve loading sequencing problem. In this approach, a simulation with different complexity scale is introduced to emulate the loading process. With real world data training, the proposed PERDDQN can solve loading sequencing episode in seconds. Result shows good efficiency and generalization of proposed algorithm. Simple simulation trained network evaluated by complex simulation shows that the proposed approach could be applicable to real word scheduling.

Automated Guided Vehicles (AGVs) for automated container terminals are mainly used for horizontal transportation at the forefront of the terminal. They shoulder the responsibility of container transportation between the quay cranes and yard cranes. Optimizing their scheduling can not only improve operational efficiency, but also help reduce energy consumption and promote green development of the port. This article first constructs a mathematical model with the goal of minimizing the total energy consumption of AGVs, considering the impact of different states of AGVs on energy consumption during operation. Secondly, by using the variable neighborhood search algorithm, the AGV allocation for container operation tasks is optimized, and the operation sequence is adjusted to reduce energy consumption. The algorithm introduces five types of operators and a random operator usage order to expand the search range and avoid local optima. Finally, the influence of the number and speed of AGVs on the total energy consumption is discussed, and the optimization performance of the variable neighborhood search algorithm and genetic algorithm is compared through computational experiments. The research results show that the model and variable neighborhood search algorithm proposed in this paper have a significant effect on reducing the total energy consumption of AGVs and show good stability and practical application potential.

Yang, X.; Zhao, N., and Yu, S., 2020. Combined internal trucks allocation of multiple container terminals with Hungarian Algorithm. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 923–927. Coconut Creek (Florida), ISSN 0749-0208.With the rising efficiency demand and intelligence trend of the container terminals (CTs), horizontal transportation could be a vital bottle neck of terminal operations. In artificial terminals, Internal trucks (ITs) is the most common and versatile horizontal equipment, the efficiency of ITs affects horizontal transportation efficiency greatly. With sharing ITs among multiple CTs located nearby, the IT resource could be allocated in a wider range to further improve handling efficiency. In this paper, we proposed a multi-objective MIP model for IT allocation, and a Hungarian Algorithm is introduced to solve the problem in a preferable period of time. Numerical experiment shows that, the proposed model and Hungarian Algorithm are efficient to achieve the optimal allocation solution.