Strategic planning of seaport hinterland networks with focus on LCL shipment consoldiation in gateways

全球化导致商品数量迅速增长。这在洲际运输路线上尤其明显。因此，海洋货物运输供应商面临的挑战是如何在高效的运输网络中安排其集装箱的路线。由于海洋货物运输供应商依赖于海洋承运人的能力，它们的重点是改善成本密集的海港腹地服务的自组织网络结构。到目前为止，各个运输环节都是独立规划的。这通常导致小于集装箱载荷（LCL）的海运货物集装箱的较差的容量利用率。因此，港口腹地网络只能达到低效率。然而，日益激烈的竞争要求更有效地利用能力的装载单位。为了提高能力利用率，重要的是要考虑整个运输网络，包括海港腹地交通。内陆的门户使不同关系的运输量能够有效地合并。因此，可以显著提高海运集装箱的容量利用率。 为了实现这一目标，确定一个网络结构是至关重要的，它考虑到了拼箱货物的成本效益，从而实现了一个具有成本效益的路线。鉴于上述情况，本知识转移项目的目的是通过考虑拼箱货物的成本效益，发展创新的网络结构，为海港腹地交通。在之前的DFG项目中，两个申请人密切合作，为经典的LCL网络开发的优化方法将适用于海港腹地网络。此外，还将开发新的优化技术，以便为使用内陆门户的海上运输制定更实用的配方。一个重要的挑战是将随机行为（如需求随时间的波动）整合到我们的方法中。因此，我们将建立多阶段随机和鲁棒模型，并使用分解技术解决它们的最优性。通过本项目，我们不仅要改进考虑随机行为的数学优化方法，而且要解决现实的交通规划问题。我们的项目合作伙伴德铁信可作为全球领先的物流供应商，为在实践中测试开发的优化工具提供了理想的环境。

Globalization results in a rapidly growing volume of goods. This is observed in particular on intercontinental transport routes. Therefore, ocean freight transport providers face the challenge of routing their containers in efficient transport networks. Since ocean freight transport providers rely on the capacities of ocean carriers, their focus is on improving the self-organized network structure of the cost-intensive seaport hinterland services. Until now, the individual transport links are planned independently. This generally results in a poor capacity utilization of less-than-container load (LCL) ocean freight containers. Thus, seaport hinterland networks can only achieve low efficiency.However, the increasing competition requires a more efficient capacity utilization of the loading units. In order to improve the capacity utilization it is important to consider the entire transport network including the seaport hinterland traffic. Gateways in the hinterland enable efficient consolidation of transport volumes of different relations. Thus, capacity utilization of the ocean freight containers can be significantly improved. In order to achieve this, it is crucial to determine a network structure, which considers consolidation effects enabling a cost-efficient routing of the LCL shipments.In the view of the above, the aim of this knowledge transfer project is to evolve innovative network structures for seaport hinterland traffic by taking the cost-efficient consolidation of LCL shipments into account. Optimization methods, which were developed for classical LCL-networks during the preceding DFG project in close cooperation between the two applicants, will be adapted to the context of seaport hinterland network. In addition, new optimization techniques will be developed for more applied formulations for maritime transports using gateways in the hinterland. An important challenge is the integration of stochastic behavior like demand fluctuations over time into our methods. Therefore, we will set up multi-stage stochastic and robust models and solve them to optimality with use of decomposition techniques.With this project, we want not only to refine mathematical optimization methods under consideration of stochastic behavior, but also we want to solve realistic transport planning problems. Our project partner DB Schenker offers as the world's leading logistics provider the ideal environment for testing the developed optimization tools in practice.