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：本文提出了一种面向真实世界装箱问题的基准数据集。该数据集包含12个具有不同复杂度等级的实例，其复杂性体现在规模（包裹数量从38到53不等）和用户自定义需求两方面。在构建这些实例时，我们考虑了多项现实导向约束：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脚本用于数据集生成，命名为Q4RealBPP-DataGen。该基准最初设计用于评估量子求解器的性能，因此实例特征根据当前量子设备的局限性进行定制。此外，数据集生成器支持构建通用型基准。本文所提供的数据将成为推动量子计算研究人员解决真实世界装箱问题的基线参考。