Benchmark dataset and instance generator for Real-World Three-Dimensional Bin Packing Problems
This provides a baseline for quantum computing researchers to work on real-world bin packing problems, but it is incremental as it builds on existing benchmark concepts.
The authors tackled the lack of real-world benchmarks for three-dimensional bin packing problems by proposing a dataset of 12 instances with varying complexity and real-world restrictions, and they developed a Python script for dataset generation to facilitate research, particularly for evaluating quantum solvers.
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.