In logistics and manufacturing, it is often the case that items or objects must be optimally positioned within a 3D volume (container). This is a challenging optimization task, which becomes even more challenging when the items or objects in question have irregular shapes and can be rotated in multiple ways. This problem of positioning 3D objects within a 3D volume can be seen as an optimization problem. More specifically, it belongs to the class of 3D Cutting & Packing optimization problems. These problems are unique in the fact that they combine geometric computations with (combinatorial) optimization. Some of the most challenging cutting & packing problems are those featuring 3D irregular objects.

In this research project, we would like to advance the state-of-the-art in solving 3D irregular cutting & packing problems. We will develop highly parallel optimization algorithms and run them on modern computing hardware such as multicore CPUs and GPUs. Furthermore, we will develop efficient optimization algorithms that can run locally on hardware with limited resources.