Project Description


The aim of the project is to reduce unproductive container reshuffling, currently accounting for approximately 20% of container movements, and consequently, to release tied-up handling capacity. More efficient handling also reduces waiting times for trucks and trains, ensuring smooth operations. 

Project objectives:

Handling decisions are mathematically optimized through the implementation of a decision support system. The order data from participating freight forwarders is automatically shared with the terminal via an interface. Using this interface, a precise pickup time for a significant portion of incoming containers can be determined. For containers without order information, artificial intelligence is developed to predict a pickup time based on various container data such as content, origin, and destination. Using the freight data and AI prediction, a pickup time is determined for each container. The so-called extraction sequence, a key component for optimizing handling decisions, is derived from these pickup times.

Building on this, two optimization modules are developed. First, the initial storage is considered. Upon delivery, it is essential to find a storage space for a container so that it causes minimal or no reshuffling during later extraction. Additionally, a module is developed to rearrange the container stock during off-peak hours so that the containers are pre-sorted for the following day according to their extraction sequence, avoiding reshuffling during peak times.

The individual components of the decision support system are then integrated and tested at the TriCon Terminal in Nuremberg. The system automatically generates storage recommendations for containers arriving by truck or train in operational mode and creates an evening plan for rearranging existing containers, preparing the terminal for the next day.

Tasks of the CML:

  • Conception of the decision support system
  • Development of an artificial intelligence for predicting container dwell times
  • Development of optimization modules for container storage and reshuffling
  • Integration of individual modules into a holistic decision support system
  • Integration of the decision support system into the Terminal Operating System 

Project consortium:

TriCon Container-Terminal GmbH Nuremberg, cargo support GmbH & Co. KG, Fraunhofer CML

Smart-Stack is funded by the IHATEC funding program for innovative port technologies in the period from October 2023 to September 2026.