Efficient packing of items into bins is a common daily task. Known as Bin Packing Problem, it has been intensively studied in the field of artificial intelligence, thanks to the wide interest from industry and logistics. Since decades, many variants have been proposed, with the three-dimensional Bin Packing Problem as the closest one to real-world use cases. We introduce a hybrid quantum-classical framework for solving real-world three-dimensional Bin Packing Problems (Q4RealBPP), considering different realistic characteristics, such as: i) package and bin dimensions, ii) overweight restrictions, iii) affinities among item categories and iv) preferences for item ordering. Q4RealBPP permits the solving of real-world oriented instances of 3dBPP, contemplating restrictions well appreciated by industrial and logistics sectors.

翻译：高效地将物品装入箱中是日常工作中的常见任务，这被称为装箱问题（Bin Packing Problem），人工智能领域对它进行了深入研究，这得益于工业和物流行业的广泛关注。多年来，已经提出了许多变体，其中三维装箱问题（3dBPP）是最接近实际用例的。为解决实际三维装箱问题（3dBPP）提出了混合量子-经典算法框架（Q4RealBPP），考虑了不同的现实特征，如：i）包装和箱子尺寸，ii）超重限制，iii）物品类别之间的亲和力和iv）物品排序的偏好。Q4RealBPP允许解决考虑到工业和物流部门广泛关注的限制的3dBPP实际实例。