Classification and evaluation of the algorithms for vector bin packing

Mommessin, Clément; Erlebach, Thomas; Shakhlevich, Natalia V.

Abstract

Heuristics for Vector Bin Packing (VBP) play an important role in modern distributed computing systems and other applications aimed at optimizing the usage of multidimensional resources. In this paper we perform a systematic classification of heuristics for VBP, with the focus on construction heuristics. We bring together existing VBP algorithms and their tuning parameters, and propose new algorithms and new tuning parameters. For a less studied class of multi-bin algorithms, we explore their properties analytically, considering monotonic and anomalous behavior and approximation guarantees. For empirical evaluation, all algorithms are implemented as the Vectorpack library and assessed through extensive experiments. Our findings may serve as the basis for the development of more complex, hybrid algorithms, hyperheuristics and machine learning algorithms. The Vectorpack library can also be adjusted for addressing enhanced VBP problems with additional features, which arise in applications, especially those typical for modern distributed computing systems.

Citation

Mommessin, C., Erlebach, T., & Shakhlevich, N. V. (2025). Classification and evaluation of the algorithms for vector bin packing. Computers and Operations Research, 173, Article 106860. https://doi.org/10.1016/j.cor.2024.106860

• Journal Article Type: Article
• Acceptance Date: Sep 30, 2024
• Online Publication Date: Oct 5, 2024
• Publication Date: 2025-01
• Deposit Date: Oct 5, 2024
• Publicly Available Date: Oct 7, 2024
• Journal: Computers & Operations Research
• Print ISSN: 0305-0548
• Electronic ISSN: 1873-765X
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 173
• Article Number: 106860
• DOI: https://doi.org/10.1016/j.cor.2024.106860
• Public URL: https://durham-repository.worktribe.com/output/2949011