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Tugboat scheduling for container ports

机译：拖船调度容器端口

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摘要

Container ports play an important role in global logistics and supply chains by providing container handling services for container ships. In a port, container ships usually need the assistance of tugboats in order to undergo berthing, shifting, and unberthing movements. Effective scheduling of the tugboats for serving the ships is thus of great importance for ensuring safe and efficient container ship movements in a port. However, research on tugboat scheduling is scarce in the literature. We contribute to the literature by studying a real-life tugboat scheduling problem (Tug-SP). We formulate the Tug-SP using a mixed-integer linear programming (MILP) model taking into consideration various practical constraints. In view of the specific problem structure, we further develop six families of valid inequalities to strengthen the MILP model. To efficiently solve the Tug-SP, we develop a tailored branch-and-cut algorithm by incorporating these valid inequalities into a standard branch-and-bound solution framework. We evaluate the computational performance of the proposed branch-and-cut algorithm using a set of test instances generated from real-life ship traffic data collected from the Port of Singapore. The computational results validate the effectiveness of the proposed valid inequalities, and show that the branch-and-cut algorithm can optimally solve instances of realistic sizes with a reasonable amount of computation time.
机译：集装箱港口通过为集装箱船舶提供集装箱处理服务，在全球物流和供应链中发挥着重要作用。在港口中，集装箱船通常需要拖船的帮助，以便接受停靠，移位和张力运动。因此，拖船用于服务船舶的拖船的有效调度对于确保端口中的安全和有效的集装箱船移动非常重要。然而，关于拖船调度的研究在文献中稀缺。我们通过研究现实生活拖船调度问题（Tug-SP）来促进文献。考虑到各种实际限制，我们使用混合整数线性编程（MILP）模型制定Tug-SP。鉴于具体的问题结构，我们进一步开发了六个有效不等式的家庭，以加强摩尔普模型。为了有效地解决TUG-SP，我们通过将这些有效的不等式纳入标准分支和绑定的解决方案框架来开发定制的分支和切割算法。我们使用从新加坡港口收集的现实船舶流量数据生成的一组测试实例评估所提出的分支和切割算法的计算性能。计算结果验证了所提出的有效不等式的有效性，并表明分支和切割算法可以最佳地解决具有合理数量的计算时间的现实尺寸的实例。

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  • 来源
    《Oceanographic Literature Review》 |2020年第10期|2303-2303|共1页
  • 作者

    X. Wei; S. Jia; Q. Meng; K.C. Tan;

  • 作者单位

    Institute of Big Data Intelligent Management and Decision College of Management Shenzhen University Shenzhen 518061 China;

    Institute of Big Data Intelligent Management and Decision College of Management Shenzhen University Shenzhen 518061 China;

    Institute of Big Data Intelligent Management and Decision College of Management Shenzhen University Shenzhen 518061 China;

    Institute of Big Data Intelligent Management and Decision College of Management Shenzhen University Shenzhen 518061 China;

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