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Multiobjective hybrid genetic algorithm for quay crane scheduling in berth allocation planning

机译:泊位分配计划中码头起重机调度的多目标混合遗传算法

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With the development of the global business and logistics under the internet environment a Container Terminal (CT) system becomes more and more busy. Therefore, the available resources in the seaport get scarcer than before. In order to increase the operating efficiency of CT system, the resources planning problem has become a critical issue in the fields of operations research and logistics. In this paper, we introduce the Berth Allocation Planning (BAP) problem and formulate a multiobjective mathematical model considering each berth for container ship with different number of Quay Cranes (QCs) and balance of QC's workload. In order to solve this QC scheduling in BAP problem, we propose a multiobjective hybrid Genetic Algorithm (mohGA) approach with a priority-based encoding method. To demonstrate the effectiveness of proposed mohGA approach, numerical experiment is carried out and the best solution to the problem is obtained.
机译:随着互联网环境下全球业务和物流的发展,集装箱码头(CT)系统变得越来越繁忙。因此,海港中的可用资源比以前更加稀缺。为了提高CT系统的运行效率,资源规划问题已经成为运筹学和物流领域的关键问题。在本文中,我们介绍了泊位分配计划(BAP)问题,并考虑了具有不同数量的码头起重机(QC)的集装箱船的每个泊位以及QC工作量的平衡,制定了一个多目标数学模型。为了解决BAP问题中的这种QC调度问题,我们提出了一种基于优先级编码的多目标混合遗传算法(mohGA)。为了证明所提出的mohGA方法的有效性,进行了数值实验,并获得了对该问题的最佳解决方案。

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