In this paper, a benchmark for real-world bin packing problems is proposed. This dataset is composed of 12 instances comprehending different levels of problem complexity regarding size (with the number of packages ranging from 38 to 53) and user-defined requirements. In fact, several real-world oriented restrictions have been considered for building these instances: i) items and bins dimensions, ii) weight restrictions, iii) affinities among packages categories iv) preferences for package ordering and v) load balancing. Besides the data, we also provide an own-developed Python script for the dataset generation, coined as Q4RealBPP-DataGen. The benchmark was firstly proposed to evaluate quantum solvers, therefore the characteristic of this set of instances were designed according to the current limitations of quantum devices. Additionally, the dataset generator is included to allow the construction of general-purpose benchmarks. The data introduced on this paper provides a baseline that will encourage quantum computing researchers to work on real-world bin packing problems

翻译：本文提出了一个面向真实世界装箱问题的基准数据集。该数据集由12个实例组成，这些实例包含不同的问题复杂度级别，涉及规模（包裹数量从38到53不等）以及用户自定义需求。具体而言，在构建这些实例时考虑了多种真实世界导向的约束条件：i) 物品与箱子尺寸，ii) 重量限制，iii) 包裹类别间的亲和性，iv) 包裹排序偏好，以及v) 负载均衡。除数据本身外，我们还提供了一个自主开发的Python脚本用于数据集生成，命名为Q4RealBPP-DataGen。该基准最初旨在评估量子求解器，因此这组实例的特性是根据当前量子设备的局限性设计的。此外，该数据集生成器允许构建通用基准。本文所引入的数据为量子计算研究人员研究真实世界装箱问题提供了基线。