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：将物品高效装入容器是常见的日常任务。作为装箱问题，它因受到工业与物流领域的广泛关注而在人工智能领域得到了深入研究。数十年来，研究者已提出多种变体，其中三维装箱问题最贴近现实应用场景。我们提出一种混合量子-经典框架（Q4RealBPP），用于解决现实场景下的三维装箱问题，综合考虑了以下实际特性：i）包装箱与容器尺寸，ii）超重限制，iii）货物类别间的相容性，iv）物品装载顺序偏好。Q4RealBPP能够求解面向现实场景的三维装箱问题实例，并充分考虑了工业与物流领域高度关注的实际约束条件。