Sequential decision-making is a critical process that governs the loading and unloading operations of container terminals through a series of instructions. The quality of these batch-level dispatching decisions directly determines operational efficiency and the competitive position of the terminal. This paper proposes an intelligent instruction sequencing model based on a Parallel Priority-driven Deep Q-Network (ppDDQN) algorithm, acting as a centralized instruction dispatcher. The proposed ppDDQN extends standard Double DQN by incorporating (i) a parallel priority mechanism that integrates five domain-specific decision features—yard crane movement, container flipping, loading sequence, operational conflicts, and task completion potential—into the experience replay prioritization, and (ii) a wake–sleep feature learning architecture for structured state representation. A comprehensive simulation study with 100 validation cases across three scales (29, 50, and 100 containers) demonstrates that ppDDQN achieves a 20.0% improvement in normalized objective value over the genetic algorithm baseline and a 9.0% improvement over the step-activation variant (with statistical significance p < 0.01), while maintaining feasibility in over 96% of test cases across all scales. The proposed method effectively mitigates yard crane travel distance, limits container rehandling, and resolves operational conflicts, providing a robust batch-level sequencing solution under multi-equipment operational constraints.
Du et al. (Tue,) studied this question.