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

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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.