Task scheduling for vehicle–crane coordination is crucial for the operational efficiency of electrified automated container terminals (ACTs). However, under fully shared dispatching, existing studies rarely capture how charging-induced capacity fluctuations disrupt bidirectional service–arrival matching and propagate service-window shifts. To address this gap, this study proposes a comprehensive spatio-temporal optimization approach. Firstly, a bi-objective model is established to minimize service–arrival mismatch and vehicle energy consumption under state-of-charge (SOC) and charger-capacity constraints, explicitly quantifying vehicle–crane alignment at both handling interfaces. Secondly, an enhanced multi-objective algorithm (ST-NSGA-II) is developed, integrating a feasibility-preserving recursive decoding mechanism and a spatio-temporal variable neighborhood search (VNS) procedure. Finally, numerical experiments demonstrate that ST-NSGA-II significantly reduces mismatch and energy consumption compared to standard NSGA-II in large-scale scenarios. It also outperforms MOEA/D in Pareto-set quality, yielding a higher hypervolume (1.301 vs. 0.960) and a lower Spacing value (0.102 vs. 0.185). The results demonstrate that the proposed spatio-temporal optimization approach can effectively reduce handover mismatch compared to conventional scheduling modes, thereby achieving seamless task scheduling for vehicle–crane coordination.
Wang et al. (Thu,) studied this question.
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