Storage space allocation in container terminals with mixed storage mode under uncertain conditions

Container terminals are facilities where containers are loaded and unloaded from ships, trucks, and trains. They are essential nodes in the global supply chain network, and their efficiency affects the performance of the entire system. One of the key factors that determines the efficiency of a container terminal is the storage space allocation, which is the problem of assigning containers to specific locations in the yard. The storage space allocation problem is challenging because it involves many constraints and objectives, such as minimizing the handling cost, maximizing the space utilization, and satisfying the service level agreements.

In recent years, some container terminals have adopted a mixed storage mode, which allows containers to be stored in both horizontal and vertical stacks. This mode can increase the storage capacity and flexibility of the terminal, but it also introduces new uncertainties and complexities to the storage space allocation problem. For example, the arrival and departure times of containers may be uncertain due to external factors such as weather, traffic, or customs. Moreover, the stacking height and stability of containers may vary depending on their dimensions, weights, and types.

In this paper, we propose a novel approach to address the storage space allocation problem in container terminals with mixed storage mode under uncertain conditions. We formulate the problem as a stochastic programming model that incorporates both horizontal and vertical stacking constraints, as well as uncertainty in container arrival and departure times. We develop a solution algorithm that combines scenario generation, decomposition, and heuristic techniques to obtain near-optimal solutions within reasonable computational time. We conduct extensive numerical experiments to evaluate the performance of our approach on various instances with different characteristics and levels of uncertainty. The results show that our approach can significantly reduce the expected total cost and improve the service level of the terminal compared to existing methods.

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