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The heavy congestion at the global ports and the impact of the pandemic spread make the automated container terminal competitive. With the operations of more and more automated container terminals, the optimization of yard space allocation becomes one of the most urgent issues for improving the handling efficiency for the automated container terminal. To figure out the optimal clustering strategy to allocate the containers in the automated container terminal, this study investigates the stacks allocation with different scenarios. A novel stack-based allocation model is built to balance both the transportation distance and the allocation dispersion among blocks. Based on the model we built, we talked about the impact of efficiency of automated rail-mounted gantry crane and the berthing time for each vessel on the stacks allocation. The results show that the stack-based allocation with multi-batches in the same bay is suitable for the automated container terminal, while both the efficiency of the automated rail-mounted gantry crane and the berthing time will directly affect the total transportation distance for allocating the containers. The research is very inspiring for the terminal manager to choose the suitable clustering strategy and improve the yard space management for the automated container terminal. [ABSTRACT FROM AUTHOR]

Adverse weather condition is one of the inducements that lead to supply uncertainty of an urban transportation system, while travelers' multiple route choice criteria are the nonignorable reason resulting in demand uncertainty. This paper proposes a novel stochastic traffic network equilibrium model considering impacts of adverse weather conditions on roadway capacity and route choice criteria of two-class mixed roadway travellers on demand modes, in which the two-class route choice criteria root in travelers' different network information levels (NILs). The actual route travel time (ARTT) and perceived route travel time (PRTT) are considered as the route choice criteria of travelers with perfect information (TPI) and travelers with bounded information (TBI) under adverse weather conditions, respectively. We then formulate the user equilibrium (UE) traffic assignment model in a variational inequality problem and propose a solution algorithm. Numerical examples including a small triangle network and the Sioux Falls network are presented to testify the validity of the model and to clarify the inner mechanism of the two-class UE model under adverse weather conditions. Managerial implications and applications are also proposed based on our findings to improve the operation efficiency of urban roadway network under adverse weather conditions., 1. Introduction Uncertainties of transportation networks are logically derived from supply side (roadway capacity fluctuation) and demand side (travel demand variation) [1-4], which can also be considered as exogenous and [...]

This article investigates the generation of a resilient yard template, which has the property of resilience under storage demand fluctuations. A stochastic programming model is formulated aiming at minimizing the risk of containers without available storage slots being assigned in the designated area. Furthermore, a corresponding particle swarm optimization algorithm is proposed for solving the model. Finally, numerical experiments are conducted to verify the effectiveness of the proposed model and solution approaches. [ABSTRACT FROM AUTHOR]

The degree of container dispersion at a transhipment terminal is measured by an index termed container location dispersion, which represents the range, both horizontal and vertical, of all containers for ship loading that are scattered in the yard. First, this paper identifies the impacts of container location dispersion on loading performance with insufficient or sufficient equipment deployment in real-life settings at a terminal. Then, we investigate those scenarios with different levels of interference. This research aims to explore the relationship between the container location dispersion and Gross Crane Rate (GCR) stability at a transhipment terminal and proposes a discrete-event driven simulation model for this purpose. All data in our experiments are extracted from the terminal operation system at a real-life container terminal. It is concluded from a series of experiments that the container location dispersion well captures the overall performance of container terminal handling and can be used for yard template optimization and management. Implications of this investigation are discussed in the context of the yard template design and improvement in the overall performance of a container transhipment terminal. [ABSTRACT FROM AUTHOR]