Multi-Objective Packing of 3D Objects into Arbitrary Containers

Packing problems arise in numerous real-world applications and often take diverse forms. We focus on the relatively underexplored task of packing a set of arbitrary 3D objects-drawn from a predefined distribution-into a single arbitrary 3D container. We simultaneously optimize two potentially conflicting objectives: maximizing the packed volume and maintaining sufficient spacing among objects of the same type to prevent clustering. We present an algorithm to compute solutions to this challenging problem heuristically. Our approach is a flexible two-tier pipeline that computes and refines an initial arrangement. Our results confirm that this approach achieves dense packings across various objects and container shapes.