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。这种情况在洲际运输路线上尤其明显。因此，海运供应商面临着在高效的运输网络中安排集装箱路线的挑战。由于海洋货物运输供应商依赖海运承运人的能力，他们的重点是改善自组织的网络结构的成本密集型海港腹地服务。到目前为止，各个交通线路都是独立规划的。这通常会导致少集装箱装载（LCL）海运集装箱的能力利用率较差。因此，海港腹地网络只能实现低效率。然而，日益激烈的竞争要求更有效地利用装载单元的容量。为了提高运力利用率，必须考虑包括海港腹地交通在内的整个运输网。腹地的门户可以有效地整合不同关系的运输量。从而显著提高海运集装箱的运力利用率。为了实现这一目标，确定一个网络结构是至关重要的，它考虑了整合效应，使拼箱货物的路线具有成本效益。综上所述，这个知识转移项目的目的是考虑到拼箱运输的成本效益，为海港腹地交通发展创新的网络结构。在之前的DFG项目中，在两个申请人的密切合作下，为经典的lcl网络开发的优化方法将适应海港腹地网络的背景。此外，还将开发新的优化技术，为使用内陆门户的海上运输提供更多的应用配方。一个重要的挑战是将随机行为（如需求随时间波动）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，并利用分解技术求解它们的最优性。在这个项目中，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。作为世界领先的物流供应商，我们的项目合作伙伴德铁信可为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.