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实际装箱问题的基准数据集。该数据集包含12个不同复杂程度的实例，涉及规模（包裹数量范围为38至53个）及用户自定义需求两个维度。具体而言，构建这些实例时考虑了多项面向实际应用的约束条件：一）物品与货箱的尺寸规格，二）重量限制，三）包裹类别间的关联性，四）包裹排序优先级，五）负载均衡。除数据本身外，我们还提供了自主研发的Python脚本用于数据集生成，并将其命名为Q4RealBPP-DataGen。该基准最初用于评估量子求解器的性能，因此其实例特性依据当前量子设备的局限性进行设计。此外，附带的数据集生成器支持构建通用型基准。本文所提出的数据将为量子计算研究者探索实际装箱问题提供基准参照。