This paper addresses the optimization of container unloading and loading operations at ports, integrating quay-crane dual-cycling with dockyard rehandle minimization. We present a unified model encompassing both operations: ship container unloading and loading by quay crane, and the other is reducing dockyard rehandles while loading the ship. We recognize that optimizing one aspect in isolation can lead to suboptimal outcomes due to interdependencies. Specifically, optimizing unloading sequences for minimal operation time may inadvertently increase dockyard rehandles during loading and vice versa. To address this NP-hard problem, we propose a hybrid genetic algorithm (GA) QCDC-DR-GA comprising one-dimensional and two-dimensional GA components. Our model, QCDC-DR-GA, consistently outperforms four state-of-the-art methods in maximizing dual cycles and minimizing dockyard rehandles. Compared to those methods, it reduced 15-20% of total operation time for large vessels. Statistical validation through a two-tailed paired t-test confirms the superiority of QCDC-DR-GA at a 5% significance level. The approach effectively combines QCDC optimization with dockyard rehandle minimization, optimizing the total unloading-loading time. Results underscore the inefficiency of separately optimizing QCDC and dockyard rehandles. Fragmented approaches, such as QCDC Scheduling Optimized by bi-level GA and GA-ILSRS (Scenario 2), show limited improvement compared to QCDC-DR-GA. As in GA-ILSRS (Scenario 1), neglecting dual-cycle optimization leads to inferior performance than QCDC-DR-GA. This emphasizes the necessity of simultaneously considering both aspects for optimal resource utilization and overall operational efficiency.

翻译：本文研究港口集装箱装卸作业优化问题，将岸桥双循环操作与堆场翻箱最小化进行集成。我们提出了一个统一模型，涵盖两项核心操作：一是通过岸桥进行船舶集装箱的卸载与装载，二是在装船过程中减少堆场翻箱操作。我们认识到，由于操作间的相互依赖关系，孤立地优化单个方面可能导致次优结果。具体而言，为最小化作业时间而优化的卸载序列可能无意中增加装载过程中的堆场翻箱，反之亦然。针对这一NP难问题，我们提出了一种混合遗传算法QCDC-DR-GA，包含一维与二维遗传算法组件。我们的QCDC-DR-GA模型在最大化双循环次数和最小化堆场翻箱方面持续优于四种先进方法。相较于这些方法，该算法为大型船舶减少了15-20%的总作业时间。通过双尾配对t检验的统计验证，在5%显著性水平上证实了QCDC-DR-GA的优越性。该方法有效结合了岸桥双循环优化与堆场翻箱最小化，实现了装卸总时间的整体优化。结果凸显了分别优化岸桥双循环与堆场翻箱的低效性。碎片化方法（如通过双层遗传算法优化的岸桥双循环调度以及GA-ILSRS（情景2））相较于QCDC-DR-GA仅显示出有限改进。正如GA-ILSRS（情景1）所示，忽略双循环优化将导致性能劣于QCDC-DR-GA。这强调了必须同时考虑两方面因素以实现资源最优配置与整体运营效率。