U-shaped automated container terminals (ACTs) represent a strategic design in port infrastructure that facilitates simultaneous loading and unloading operations. This paper addresses the challenges of scheduling multiple types of equipment, such as dual trolley quay cranes (DTQCs), automated guided vehicles (AGVs), double cantilever rail cranes (DCRCs), and external trucks (ETs) in U-shaped ACTs. This paper proposes a mixed integer linear programming model for optimizing the multiple equipment scheduling, aiming to minimize container completion time and AGV waiting time simultaneously. This paper customizes a hybrid genetic-cuckoo optimization algorithm (HGCOA) with double-point crossover and Lévy flight Cuckoo search strategies. Extensive numerical results show that the proposed HGCOA outperforms the benchmark genetic algorithms in terms of solution quality and computational time while significantly improving efficiency without substantial sacrifices in solution quality compared with the exact solution method. Overall, this study presents a promising solution for enhancing coordination and operation efficiency in U-shaped ACTs.

中文翻译：

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考虑装卸作业的U型自动化集装箱码头多设备调度优化


U 形自动化集装箱码头（ACT）代表了港口基础设施的战略设计，可促进同步装卸作业。本文解决了在 U 形 ACT 中调度多种类型设备的挑战，例如双小车码头起重机 (DTQC)、自动导引车 (AGV)、双悬臂轨道起重机 (DCRC) 和外部卡车 (ET)。本文提出了一种用于优化多设备调度的混合整数线性规划模型，旨在同时最小化集装箱完成时间和AGV等待时间。本文定制了一种具有双点交叉和Lévy飞行布谷鸟搜索策略的混合遗传布谷鸟优化算法（HGCOA）。大量的数值结果表明，与精确求解方法相比，所提出的 HGCOA 在解质量和计算时间方面优于基准遗传算法，同时显着提高了效率，且没有大幅牺牲解质量。总的来说，这项研究为提高 U 形 ACT 的协调和运营效率提供了一个有前景的解决方案