In this article, a benchmark for real-world bin packing problems is proposed. This dataset consists of 12 instances of varying levels of complexity regarding size (with the number of packages ranging from 38 to 53) and user-defined requirements. In fact, several real-world-oriented restrictions were taken into account to build these instances: i) item and bin dimensions, ii) weight restrictions, iii) affinities among package categories iv) preferences for package ordering and v) load balancing. Besides the data, we also offer an own developed Python script for the dataset generation, coined Q4RealBPP-DataGen. The benchmark was initially proposed to evaluate the performance of quantum solvers. Therefore, the characteristics 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 in this article provides a baseline that will encourage quantum computing researchers to work on real-world bin packing problems.

翻译：本文提出了一套面向真实世界装箱问题的基准数据集。该数据集包含12个实例，其复杂度因规模（包裹数量介于38至53之间）及用户定义需求而异。具体而言，构建这些实例时考虑了多项面向真实世界的约束条件：i) 物品与箱子尺寸，ii) 重量限制，iii) 包裹类别间的关联性，iv) 包裹排序偏好，以及v) 负载均衡。除数据外，我们还提供自主开发的Python脚本（命名为Q4RealBPP-DataGen）用于生成该数据集。该基准最初旨在评估量子求解器的性能，因此实例特性根据当前量子设备的局限性而设计。此外，通过数据集生成器可构建通用基准。本文所介绍的数据为量子计算研究者攻克真实世界装箱问题提供了基线支撑。