Benchmark dataset and instance generator for real-world three-dimensional bin packing problems

被引:4
|
作者
Osaba, Eneko [1 ]
Villar-Rodriguez, Esther [1 ]
Romero, Sebastian V. [1 ]
机构
[1] TECNALIA, Basque Res & Technol Alliance BRTA, Derio 48160, Spain
来源
DATA IN BRIEF | 2023年 / 49卷
关键词
Optimization; Bin packing problem; Quantum computing; Quantum annealer; Operations research;
D O I
10.1016/j.dib.2023.109309
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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. (c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
引用
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页数:10
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