Artificial Intelligence for Improved Container Loading
Efficiency (AI4CL)
This project is funded by the Austrian Research Promotion Agency (FFG) within
the AI for Green funding call. The project is a collaboration between the
Combinatorial Optimization group
at Graz University of Technology and the start-up S2data. The project is planned to start in Summer/Autumn 2024 and will run for 3 years.
Project goals
The goal of the project is to develop new algorithms for container loading problems. The task
there is to find an efficient placement of boxes into transport containers such that special loading
constraints (e.g., box stacking rules, weight distribution rules, rules about the placement location
of dangerous goods) are satisfied. A special focus within this project will be on achieving more
efficient and greener transports.
From a methodological point of view, the project will involve approaches from different areas of
mathematical optimization, computer science and operations research. The developed suite of algorithms will vary strongly in their mathematical foundation. Techniques that we envisage to make
use of include mixed-integer programming, column generation, local search algorithms, metaheuristics, tree search
based algorithms with neural network state-evaluations and constraint generation approaches. The
developed algorithms will be tested and evaluated not only on existing academic benchmark data
but also on real industry data.
Project staff
At TU Graz
- Eranda Dragoti-Cela
- Bettina Klinz
- N.N. (PhD student)
At S2data
- Stefan Lendl
- Paul Tabatabai
- N.N.
Project news
Spring 2024: We are hiring a PhD student. The announcement can be downloaded
here.