A digitalization project in combined transport (CT) aims to speed up the allocation between intermodal loading units and rail wagons. With the help of suitable calculation methods and the use of methods from the field of artificial intelligence (AI), the loading of loading units onto rail wagons is to be optimized.

Project with many partners

The "KIBA" project, whose project title stands for "Artificial Intelligence and Discrete Loading Optimization Models to Increase Capacity Utilization in Combined Transport", was launched in autumn 2022. Federal Minister Volker Wissing presented the project team with the funding certificate. The KIBA IT project will run for three years and is being funded with a total of 2.34 million euros as part of the Artificial Intelligence in Mobility innovation campaign. In addition to the project coordinator Kombiverkehr Deutsche Gesellschaft für kombinierten Güterverkehr, the project partners include the Technical University of Darmstadt, Deutsche Umschlaggesellschaft Schiene-Straße (DUSS), Goethe University Frankfurt, VTG Rail Europe, INFORM and Kombiconsult.

Continental combined transport is heterogeneous in terms of loading unit types and rail wagons used. The major challenge is therefore to determine a valid and optimized allocation between the loading units and the rail wagons according to various target variables.

Challenge: many different load units

In addition to the static-technical characteristics of the semi-trailers, containers and swap bodies in their various designs and rail wagons, variable parameters such as the actual weight and type of load as well as the timetable also play a role. The aim of the project is to ensure that every request for the loading of a load unit receives a proposal for the optimum placement of the load unit on a set of wagons within the shortest possible time - at a time before all the information is available about which other load units for the same start-destination relation will still arrive at the dispatch terminal before departure. Loading optimization is intended to further advance the capacity management of intermodal operators with their extensive networks.