Benchmark dataset for logistic-oriented Bin Packing Problems

Published: 11 May 2023| Version 1 | DOI: 10.17632/9ts4rvkc5s.1


A benchmark for logistic-oriented bin packing problems is proposed. This dataset is composed of 10 instances comprehending different levels of problem complexity regarding size and user-defined requirements. Instances comprising the 1dBPP, 2dBPP, and 3dBPP are included. Several real-world oriented restrictions have been considered for building these instances: i) item and bin dimensions; ii) weight restrictions; iii) affinities among package categories; iv) preferences for package ordering; v) load balancing; vi) heterogeneous bins; and vii) item-bin associations. The benchmark was first proposed to evaluate quantum solvers, therefore, the characteristics of this set of instances were designed according to the limitations of quantum devices in 2023. Additionally, we include the results obtained for each instance. The data introduced in this benchmark provides a baseline that will encourage quantum computing researchers to work on logistic-oriented bin packing problems.



Fundacion Tecnalia Research and Innovation


Artificial Intelligence, Quantum Computing, Combinatorial Optimization, Packing Problem


Eusko Jaurlaritza

BRTA-QUANTUM project, KK-2022/00041

Centro para el Desarrollo Tecnológico Industrial

Plan complementario comunicación cuántica (EXP. 2022/01341) (A/20220551)

Eusko Jaurlaritza

Q4_Real project, ZE-2022/00033