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Integrated berth allocation and quay crane assignment with maintenance activities

机译:综合泊位分配和Quay起重机分配维护活动

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With the rapid development of worldwide container trade, quay cranes and other resources in container terminals are suffering from increasing workloads. To reduce their failure probabilities, quay cranes usually require preventive maintenance. However, these important activities are often ignored during quay crane planning in literature. This work studies an integrated berth allocation, quay crane assignment and specific quay crane assignment problem where quay crane maintenance is involved, and establishes an integer linear programming with the objective of minimising the total turnaround time. Due to the NP-hardness of the problem, CPLEX only solves instances with up to 18 vessels. For solving larger instances, we propose Left-and-Right Vessel Move (LRVM) algorithm and improved Genetic Algorithm (GA). Experimental results reveal that GA slightly outperforms LRVM with respect to solution quality as the solution of LRVM serves as one of the initial chromosomes of GA. LRVM can output feasible solutions within seconds, even for instances with 50 vessels. We further present a sensitivity analysis of preferred berth segments, and make numerical comparison on algorithm performance for the relevant model without quay crane maintenance. Both the models and proposed heuristics in this work help in dealing with the operational management of container terminal resources in practice.
机译:随着全球集装箱贸易的快速发展,集装箱码头中的码头起重机和其他资源正在遭受增加的工作量。为减少失效概率,码头起重机通常需要预防性维护。但是,这些重要的活动通常在文献中的码头起重机规划期间忽略。这项工作研究了涉及码头起重机维护的集成泊位分配,Quay起重机分配和特定码头起重机分配问题,并建立了整数线性规划,目的是最小化总周转时间。由于问题的NP硬度,CPLEX只解决了最多18个船只的实例。为了解决较大的实例,我们提出了左右血管移动(LRVM)算法和改进的遗传算法(GA)。实验结果表明,随着LRVM的溶液作为GA的初始染色体之一,GA略高于LRVM。 LRVM可以在几秒钟内输出可行的解决方案,即使对于具有50个船只的情况。我们进一步提出了对优选的泊位段的敏感性分析,对没有码头起重机维护的相关模型进行数值比较。这项工作中的模型和拟议启发式有助于处理集装箱终端资源的运营管理。

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