Energy-Aware Integrated Scheduling for Quay Crane and IGV in Automated Container Terminal

In this study, we address the integrated scheduling problem involving quay cranes and IGVs in automated terminals. We construct a mixed-integer planning model with the aim of minimizing the total energy consumption during quay crane and IGV operations, focusing on the loading-operation mode. The model considers the impact of the actual stowage of container ships on the loading order. We propose a dimension-by-dimension mutation sparrow search algorithm to optimize the model's solution quality. Building upon the standard sparrow search algorithm, we incorporate cat mapping to enhance the diversity of the initial sparrow population. To improve global search in the early stage and local search in the later stage of the algorithm, we introduce an adaptive t-distribution mutation strategy. Finally, a total of 12 instances with container counts containing 30, 100, and 250 were designed for experiments to validate the effectiveness of the model and algorithm. The experiments demonstrate that, by appropriately increasing the number of quay cranes, configuring more than two or three IGVs can achieve optimal energy consumption for overall operations.