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.

Driven by the increasing reliance on maritime transportation, achieving energy efficiency and reducing emissions in automated container terminals (ACTs) has become an urgent priority. The complex structure of a low-carbon terminal, along with the interdependent relationships between ACT container operation system and energy system, presents significant challenges to container terminal management. This issue has not yet been discussed in the existing research literature. To this end, this paper introduces a co-optimization problem of the operation, logistics, and energy (CPOL&E) based on the coupling mechanism between multiple subsystems in the ACTs. The CPOL&E considers energy recovery, where the cranes equipped with super-capacitors can recover energy during container unloading and release the stored energy during container lifting to effectively reduce the total energy consumption. A co-scheduling model is developed to simultaneously minimize the makespan and the terminal energy cost. To solve this multi-objective model, we design a multi-subpopulation differential co-evolution algorithm (MDCEA). In this algorithm, an initialization method based on heuristic rules and population entropy is introduced. The MDCEA employs a differential multi-subpopulation co-evolution strategy combined with an adaptive local search approach to enhance population performance. Through comparative experiments with three representative multi-objective algorithms, the results indicate that the MDCEA is capable of obtaining high-quality solutions and demonstrates competitive performance in solving the CPOL&E under the tested instances. Simulation results of the co-optimization mode demonstrate that the scheduling of terminal equipment significantly affects the temporal distribution of its energy demand, which in turn influences the output of the energy system.