The Bin Packing Problem is a classic problem with wide industrial applicability. In fact, the efficient packing of items into bins is one of the toughest challenges in many logistic corporations and is a critical issue for reducing storage costs or improving vehicle space allocation. In this work, we resort to our previously published quantum-classical framework known as Q4RealBPP, and elaborate on the solving of real-world oriented instances of the Bin Packing Problem. With this purpose, this paper gravitates on the following characteristics: i) the existence of heterogeneous bins, ii) the extension of the framework to solve not only three-dimensional, but also one- and two-dimensional instances of the problem, iii) requirements for item-bin associations, and iv) delivery priorities. All these features have been tested in this paper, as well as the ability of Q4RealBPP to solve real-world oriented instances.

翻译：装箱问题是一个具有广泛工业应用价值的经典问题。实际上，将物品高效装入箱子是许多物流企业面临的最大挑战之一，也是降低仓储成本或优化车辆空间分配的关键问题。本文基于我们先前提出的量子-经典框架Q4RealBPP，进一步探讨如何解决面向现实场景的装箱问题实例。为此，本文聚焦于以下特征：（i）异构箱子的存在；（ii）将该框架的求解范围从三维扩展至一维和二维装箱问题实例；（iii）物品与箱子关联的约束条件；以及（iv）交付优先级。本文对所有特征进行了测试，并验证了Q4RealBPP解决面向现实场景实例的能力。