cs.AI updates on arXiv.org 10月06日
港口集装箱优化处理:QCDC-DR-GA算法应用
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本文提出了一种结合Quay Crane Dual-Cycling(QCDC)与码头重处理最小化的混合遗传算法(QCDC-DR-GA),用于优化港口集装箱处理。实验表明,该方法能将大型船舶的总操作时间降低15-20%,且具有显著的经济效益。

arXiv:2406.08534v3 Announce Type: replace-cross Abstract: This paper addresses the NP-hard problem of optimizing container handling at ports by integrating Quay Crane Dual-Cycling (QCDC) and dockyard rehandle minimization. We realized that there are interdependencies between the unloading sequence of QCDC and the dockyard plan and propose the Quay Crane Dual Cycle - Dockyard Rehandle Genetic Algorithm (QCDC-DR-GA), a hybrid Genetic Algorithm (GA) that holistically optimizes both aspects: maximizing the number of Dual Cycles (DCs) and minimizing the number of dockyard rehandles. QCDC-DR-GA employs specialized crossover and mutation strategies. Extensive experiments on various ship sizes demonstrate that QCDC-DR-GA reduces total operation time by 15-20% for large ships compared to existing methods. Statistical validation via two-tailed paired t-tests confirms significant improvements at a 5% significance level. The results underscore the inefficiency of isolated optimization and highlight the critical need for integrated algorithms in port operations. This approach increases resource utilization and operational efficiency, offering a cost-effective solution for ports to decrease turnaround times without infrastructure investments.

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港口集装箱 QCDC算法 遗传算法 优化处理 效率提升
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